Progress 09/01/20 to 08/31/24
Outputs
Target Audience:The goal of our project is to develop a nitrogen (N) decision support tool suitable for calculating the N fertilizer requirement for any corn field in Pennsylvania. Accordingly, Pennsylvania farmers growing corn were our primary target audience. In addition, consultants, extension educators and any agricultural professionals who assist with making N fertilizer decisions would be included among those we seek to reach through our outreach activities. Through presentations/posters at a number of events/webinars we introduced the N decision support tool to a diverse group of agriculture professionals in Pennsylvania and other states as well as members of environmental/conservation NGOs and regulatory agencies. To help us test and improve the N tool, we met annually with our advisory board to vet ideas regarding the visualization, inputs, and outputs of the web dashboard interface. We worked closely with many of the farmers in this group through previous research and extension activities and they have indicated a strong interest in continuing to work with us to develop our N decision support tool. These farmers are distributed geographically across Pennsylvania and southern New York. Several are involved with farmer network groups (including the Central Susquehanna Valley Organic Crop Producers Network. Another target audience includes students with an interest in a career in organic agriculture. As noted above in the professional development section, 10 undergraduate students and 2 graduate students worked directly on this project. A final target audience was scientists interested in nitrogen decision support. We reached scientific audiences through peer reviewed publications (see "publications" section) presentations at the ASA-CSA-SSSA meetings.the Penn State Plant Science seminar (January 2023) and the Penn State Undergraduate Exhibition. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Ten undergraduate students participated in agricultural research through assisting with field and laboratory work for this project. Four undergraduates led independent research related to the N tool development, which provided opportunities to communicate with farmers and present posters at a scientific conference and an exhibition at Penn State. At Penn State, undergraduates participated in the development of a cover crop lookup table, measuring soil respiration rates across a soil texture gradient, and estimating carbon credits at the Penn State research station. At Ursinus, an undergraduate student wrote an honors thesis on communication strategies in agricultural science. Two graduate students, two laboratory technicians and a postdoctoral scholar helped to train and mentor the undergraduate students in their research experiences. We included these students in lab group meetings, which provided opportunities to listen to presentations by graduate students, discuss scientific journal articles, review draft manuscripts/presentations, and view preliminary datasets that they had helped to collect. The two graduate students (PhD candidates) used a component of this project as part of their dissertation research. Most of our project team members had several opportunities to present data collected as part of this project and to participate in outreach events. How have the results been disseminated to communities of interest?We documented our dissemination activities in by entering them all as "other products/outputs". Here we highlight some examples. We met with our farmer advisory board once per year in all years of the project. Members of our project team have presented >20 talks or posters at outreach events in which the N decision support tool was introduced to agricultural professionals in Pennsylvania and other states. We contributed to the PASA 2021 Virtual Sustainable Agriculture Conference with a recorded presentation about the principles that regulate N availability from cover crops and soil organic matter along with a demonstration of our web-based N decision support tool. This annual event typically brings together over 2,000 growers, buyers, distributors and consumers. A complete list of events is provided in the Other Products/Outputs section. We reached scientific audiences through peer reviewed publications (see "publications" section) presentations at the ASA-CSA-SSSA meetings (November 2022), the Penn State Plant Science seminar (January 2023) and the Penn State Undergraduate Exhibition (April 2023). These events allowed us to present our testing and recalibration of the biogeochemical equations underlying the N tool. We also hosted a workshop for 22 high-school students as part of the Pennsylvania School for Excellence in the Agricultural Sciences. Students visited the Penn State research station to learn about cover crops and participated in a hands-on experience processing soil samples in our campus laboratories. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
??Through this project we tested our nitrogen decision support tool on corn fields across Pennsylvania with a wide variety of cover crops and soil types. The original equations used to calculate fertilizer recommendations were developed for fine to medium-textured soils. Through this project we expanded our input dataset to include sites with coarser-textured soils and recalculated the equations. We tested the accuracy of the equations for predicting corn yield with field research at our research station in central Pennsylvania and commercial farms. In addition, we performed laboratory experiments using a wide variety of soil samples from 16 farms across Pennsylvania and southern New York. This research expanded our understanding of how soil microbes slowly release nitrogen, making it available to plants. We developed a new version of the tool that allows users to compare how the cost of different nitrogen fertilizer options affects the recommended application rates, allowing farmers to consider profitability in their decision-making. A key final product was a nitrogen decision support tool that assists farmers in choosing the optimal type and amount of fertilizer for their fields, maximizing profitability for farmers and improving environmental quality (Arrington et al. 2024 in products). We worked with farmers to test the tool on their fields to make sure it is easy to use and helpful in making fertilizer decisions.Specific accomplishments for each objective are as follows: Objective 1. Agronomic validation.Cover crops were planted at both the research station (all years) and the two on-farm sites (two years). Plots were divided into six subplots, with standard nitrogen addition levels 0, 30, 60, 120, 180, and 240 pounds N per acre as Chilean nitrate. Using these data, we recalibrated the equations underlying the tool with sites covering a broader range of soil textures, tripling the number of observations used to calibrate the original equations. Overall, the new model structure resulted in better correspondence between predicted and measured unfertilized corn yield. Generally, the newmodel structure resulted in lower predicted yields compared to the original model structure. The lower predicted yields were tied to higher model-calculated values for humification efficiency, a parameter indicating the degree to which soil microbes retain nitrogen in their biomass. We found the best new model included: 1) measured soil C:N instead of an assumed value of 10, 2) both sand and clay content, and 3) a precipitation adjustment factor. Using the original model dataset to validate the new model, we found good agreement between measured and predicted unfertilized corn yield. The new model can be applied to sites with a wide range of soil textures and is calibrated to average precipitation in the mid-Atlantic region. Objective 2 Biogeochemical Validation. This objective sought to examine whether some of the parameters we used in the model that underpins our decision support tool varied meaningfully across farms and soil types. We collected soil samples from 16 locations (commercial farms and the research station) across Pennsylvania and southern New York. We measured laboratory N mineralization rates, microbial biomass carbon and nitrogen, microbial community structure (via phospholipid fatty acid analysis), microbial carbon use efficiency, carbon nitrogen concentration, and soil texture. Carbon use efficiency is a proxy for humification efficiency (ε) (a key parameter in our N tool model) that we measure as the quantity of 18O incorporated into microbial DNA technique) across fields. We found that microbial C:N was lower and more variable than assumed in the model, ranging from 4 to 10 in the soils we collected. Microbial C:N was also strongly negatively correlated with the quantity of nitrogen mineralized over a 12-week laboratory incubation. Similarly, soil C:N was variable, ranging from 7 to 11, and negatively correlated with nitrogen mineralization To isolate the effect of soil texture of on ε, we carried out a manipulative texture experiment. We developed the technique to sieve sand from a fine-textured soil sampled from a single plot at the research station and then incrementally add the sieved sand to the original fine-textured soil to create a manipulated gradient of soil textures. We used 13C-glucose tracing methods to track the fate of added carbon through the pools of microbial biomass and soil organic matter and can then compare the 13C accumulated in these pools to the 13C lost as CO2 . Soils from the manipulated soil texture gradient displayed more consistent microbial respiration patterns than the field soils. We also found evidence for priming effects in the soils with the added glucose and alanine tracer, where microbes in soils with the added tracer mineralized additional native soil organic matter compared to soils with no added tracer. Additionally, priming effects increased with increasing sand content, indicating that there may be more scavenging and recycling of soil organic matter in sandy soils. These results were presented at the Soil Science Society of America meeting, garnering an award for student presenter Madeline Luthard. Objective 3 Outreach.Our primary outreach goal was to share the N decision support tool with farmers and solicit their feedback regarding the visualization, inputs, and outputs of the web dashboard interface. In each year of the project we shared our N decision support tool with our farmer advisory board and incorporated their suggestions into the tool development. We led and participated in many outreach events during the project (see "other products), here we highlight some examples from throughout the project. We developed a webinar as part of Penn State Extension's "Making Cover Crops Pay" series. We showed how different cover crops affect the corn yield response to nitrogen additions (based on data from the research station and on-farm sites) and demonstrated the nitrogen decision support tool. We gave two presentations at Penn State's Ag Progress days. We shared several scenarios with the audience to compare how cover crops and soil organic matter affect the tool's nitrogen fertilizer recommendations. We gave a presentation Lancaster-Lebanon Watershed Forum and Science Symposium in Elizabethtown, PA. Here we focused on the effectiveness of cover crops to reduce nitrogen leaching based on nine years of data at the Penn State research station. We presented at a farmer meetings organized by the Snyder County Conservation District and by Penn State Extension's Organic Learning Circles group. We updated our web-based N tool (https://extension.psu.edu/nitrogen-recommendations-for-corn) with the recalibrated equations developed in Objective 1. The new version of the website allows users to consider the economics of applying nitrogen fertilizer by entering the fertilizer cost and an expected price for corn. A dynamic graph illustrates how changes in the inputs affect the predicted fertilizer response curve and the N fertilizer recommendation.We added a cover crop C:N lookup table to the N decision support tool. We combined our research station dataset with a similar dataset from commercial farms across Pennsylvania, resulting in a total of approximately 1200 samples collected from 2011-2023. The table provides averages and ranges of C:N ratios for a varietyof cover crop species and mixtures. Also, it provides users with guidance on adjusting the average C:N in the table based on manure applications before or during the cover crop growth period.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Arrington, K. E., Ord��ez, R. A., Rivera-Ocasio, Z., Luthard, M., Tierney, S., Spargo, J., Finney, D., Kaye, J. P., & White, C.M. (2024). Improving a nitrogen mineralization model for predicting unfertilized corn yield. Soil Science Society of America Journal,88(3), 905-920. https://doi.org/10.1002/saj2.20665
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2023
Citation:
Mickles, Austin, "Audience Driven Scientific Communication in Agricultural Research" (2023). Environment and Sustainability Honors Papers. 13.
https://digitalcommons.ursinus.edu/environment_hon/13.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Rivera-Ocasio, Z., Montiel-Fernandez, L., King, W., Heer, P., Kaye, J. P., White, C. (2024). "Estimating Microbial Carbon Use Efficiency as the Pivot Point for a Nitrogen Decision Support Tool," Soil Science Society of America Summer Conference, San Juan, Puerto Rico.
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Progress 09/01/22 to 08/31/23
Outputs
Target Audience:Through many events over the past year, we reached our primary target audience of farmers and agricultural professionals in the mid-Atlantic region, who are potential users of the nitrogen decision support tool for corn (N tool). In February, we met with our farmer advisory board (farmers growing organic corn across Pennsylvania) to share experiences with the 2022 growing season, discuss the N tool development and plan outreach events. We introduced the N tool to both organic and conventional farmers at several outreach events, including a farmer meeting organized by the Snyder County Conservation District, a soil health field day hosted by Stroud Water Research Center and three workshops at Penn State's Ag Progress Days. We also had the opportunity to expand the geographic reach of our work through two webinars that introduced the N tool as a part of a discussion of building soil health. We had several opportunities to share our work with other researchers and the Penn State community. In November 2022, manyof our teammembers attended the ASA-CSSA-SSSA Annual Meeting in Baltimore. We shared our research findings supporting the N tool with scientists and agricultural professionals though several poster and oral presentations. In January, we presented at the Penn State Plant Science Department Seminar, which included our team's development of the N tool along with the work of our collaborators who conduct other experiments at our Penn State research station. In April, three of our students presented their independent research related to this project at the Penn State Undergraduate Exhibition. Over the past year, this project has provided many opportunities to reach our secondary target audience of students interested in organic agriculture. Ten undergraduates/recent graduates including seven at Penn State and three at Ursinus participated in a combination of field sampling, laboratory analyses, and presenting research results. Students participated in fieldwork at the Penn State research station and other farms where soil samples were collected for the laboratory incubations. We also hosted a workshop for 22 high-school students as part of the Pennsylvania School for Excellence in the Agricultural Sciences. Students visited the Penn State research station to learn about cover crops and participated in a hands-on experience processing soil samples in our campus laboratories. Changes/Problems:The winter extension meeting scheduled for December 2022 was cancelled due to the weather. We adapted our planned workshop to a shorter format, which was given each of the three days of Penn State's Ag Progress Days in August 2023. What opportunities for training and professional development has the project provided?The project has provided many opportunities for training and professional development. Ten undergraduate students participated in agricultural research through assisting with field and laboratory work for this project. Four undergraduates led independent research related to the N tool development, which provided opportunities to communicate with farmers and present posters at a scientific conference and an exhibition at Penn State. At Penn State, undergraduates participated in the development of a cover crop lookup table, measuring soil respiration rates across a soil texture gradient, and estimating carbon credits at the Penn State research station. At Ursinus, an undergraduate student wrote an honors thesis on communication strategies in agricultural science. Two graduate students, two laboratory technicians and a postdoctoral scholar helped to train and mentor the undergraduate students in their research experiences. We included these students in lab group meetings, which provided opportunities to listen to presentations by graduate students, discuss scientific journal articles, review draft manuscripts/presentations, and view preliminary datasets that they had helped to collect. The two graduate students (PhD candidates) are each leading a component of this project as part of their dissertation research. Most of our project team members have had several opportunities to present data collected as part of this project and to participate in outreach events. How have the results been disseminated to communities of interest?We have continued to work with the farmers on our advisory board to guide the development of the N tool and the outreach component of this project. This partnership includes sharing data and farmer experiences to improve the tool and identify outreach opportunities. In February 2023, we met virtually with this team to share research updates including 2022 corn yields at the on-farm sites and the research station, recalibrating the N tool model and the development of a cover crop look-up table. Over the past year, we have expanded our outreach to a broader group of farmers through a variety of in-person and virtual extension events. These events included a presentation describing the soil and cover crop data that farmers would need to use the N tool and how to obtain that information. Some of our extension events have included hands-on activities, which allowed participants to try the web-based N tool on a laptop and estimate the N content of cover crops using a handheld Greenseeker sensor (workshops at Penn State's Ag Progress Days and Penn State Extension's Agronomic Diagnostic Clinic). Farmers attending the Organic Grains Field Day visited the Penn State research station site observed visual differences of corn grown following different cover crops, reflecting their differing N contributions. Our outreach events were located at several locations across Pennsylvania as well as an online. This year, we had opportunities to share our results with other researchers through presentations at the ASA-CSA-SSSA meetings (November 2022), the Penn State Plant Science seminar (January 2023) and the Penn State Undergraduate Exhibition (April 2023). These events allowed us to present our testing and recalibration of the biogeochemical equations underlying the N tool. Members of our project team have given 20 presentations in which the N decision support tool was introduced to agricultural professionals, researchers, students, and others. These included seven oral or poster presentations at an international conference, three posters presented at a Penn State Undergraduate Exhibition, one presentation at the Penn State Plant Science seminar, two webinars and seven extension events. A complete list of events is provided in the Other Products/Outputs section. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: We will submit a manuscript to the Soil Science Society of America Journal documenting the recalibration of the N tool model. We will evaluate the updated model by comparing the unfertilized corn yield predicted by the model to observed yields in the 2022 season (at the on-farm sites and the research station). Objective 2: We will complete the analysis of εas part of the biogeochemical validation process. This will include preparation of microbial biomass 13C samples (freeze drying soil extracts and encapsulating them into small tin capsules) and sending them to Cornell Stable Isotope Laboratory for isotopic analysis (early 2024). When microbial biomass 13C and soil organic matter 13C results are obtained, they will be used, along with 13C-CO2 data, to calculate ε. From there, we will determine whether there is a relationship between soil texture and ε, leveraging results from both the inherent soil texture gradient and manipulated soil texture gradient employed in our laboratory incubation. Data analysis will be completed in summer 2023 and preparation of a publication will follow. All data from laboratory incubations conducted at Ursinus have been collected, so activities in the coming year will focus on data analysis and preparation of a publication. We will continue our analysis to identify predictors of nitrogen mineralization in the controlled laboratory incubation and use this information to further refine the N tool model. Isotope data required to quantify CUE have been received, which will allow us to proceed with the analysis of these data. We will assess temporal patterns in CUE values and determine if CUE values vary between low and high quality cover crop residues. We will also compare measured CUE values to ε values predicted by the N tool and identify soil and/or cover crop properties that may be incorporated into the model to improve estimation of ε. Objective 3: We will expand the look-up table of the spring cover crop C:N ratios by combing our research station data with the dataset used to recalibrate the N tool model. This will ensure the table includes a wide variety of cover crops grown in fields with a range of soil textures and organic matter contents. The spring cover crop C:N look-up table will be added to the web-based version of the N tool, allowing users to estimate this required input. We will complete partial budget analyses for recent years at our research station. This will provide long-term average data on the yield revenue of corn following a variety of cover crops, adjusting for the cost of seeds and planting. In addition, we will use the N addition trials at the research station in 2021 and 2022 to consider the costs of the N fertilizer compared to the revenues from yields at each N addition level. We will meet with our advisory board in winter 2024 to share research results and the updated N tool. We will continue to participate in other extension events to introduce the N tool to agricultural professionals across the mid-Atlantic region.
Impacts
What was accomplished under these goals?
Objective 1: Agronomic validation We recalibrated the model underlying the N decision support tool using a new dataset, which included the experiments in the 2021 on-farm trials. Compared to the dataset used to calibrate the original model, the new dataset included a broader range of soil textures, a more diverse set of cover crops, and a wider range of growing season precipitation. We found the best new model included: 1) measured soil C:N instead of an assumed value of 10, 2) both sand and clay content, and 3) a precipitation adjustment factor. Using the original model dataset to validate the new model, we found good agreement between measured and predicted unfertilized corn yield. The new model can be applied to sites with a wide range of soil textures and is calibrated to average precipitation in the mid-Atlantic region. We completed a second field season with the corn harvest at the on-farm sites and research station in October/November 2022. Unfertilized corn yield was consistent for both years at one of the on-farm sites, while the 2022 yields were lower than 2021 at the other on-farm site and the research station. The yield response to the N additions varied by site and previous cover crop, providing a wide range of scenarios for testing the recalibrated N tool model. Soil samples from the second year were taken to analyze the microbial carbon use efficiency and next-generation sequencing. The quantification ofthecover crop's litter decomposition and N-mineralization within the nitrogen fertilizer rate treatments acrosson-farms were conducted with the buriedlitterbagtechnique. Objective 2: Biogeochemical validation At Penn State, we continued our experiment to isolate the effect of soil texture on humification efficiency (ε) by processing soil samples and analyzing results from a 3-month incubation completed in May 2022. Samples to be analyzed for soil organic matter 13C were dried, ground, acidified to remove carbonates, and then encapsulated and sent to Cornell Stable Isotope Laboratory (completed August 2023). We analyzed the data for the 13C-CO2 gas samples from the incubation study and found that the soils from the manipulated soil texture gradient displayed more consistent microbial respiration patterns than the field soils, which validates this approach as a method to isolate the relationship between soil texture and ε. We also found evidence for priming effects in the soils with the added glucose and alanine tracer, where microbes in soils with the added tracer mineralized additional native soil organic matter compared to soils with no added tracer. Additionally, priming effects increased with increasing sand content, indicating that there may be more scavenging and recycling of soil organic matter in sandy soils. We are assessing variation in assumed and calibrated parameters of the model through laboratory incubations at Ursinus College using soil samples collected from commercial organic farms and the Penn State research station. This year, we completed laboratory incubations of soil samples from 20 fields on seven commercial farms and the research station collected in spring 2022. These samples underwent the same incubation protocols used in 2021 to determine nitrogen mineralization parameters. Soil physical, chemical, and biological properties were also measured on all soils prior to incubation. Data from the incubations conducted in 2021 and 2022 (a total of 36 unique farm fields) are currently being analyzed to assess variation in microbial C:N, soil organic matter C:N, and microbial carbon use efficiency (CUE, as measured by the 18O DNA tracer technique) across fields. We are also using these data to identify relationships among soil physical properties, soil biological properties, and nitrogen mineralization dynamics. We have found that microbial C:N was lower and more variable than assumed in the model, ranging from 4 to 10 in the soils we collected. Microbial C:N was also strongly negatively correlated with the quantity of nitrogen mineralized over a 12-week laboratory incubation. Similarly, soil C:N was variable, ranging from 7 to 11, and negatively correlated with nitrogen mineralization. To elucidate the relationship between microbial CUE and residue C:N, we conducted a laboratory incubation on a subset of soils collected in spring 2022. This incubation was carried out in parallel with the primary incubation study and under the same conditions but included measurement of microbial CUE at three time points during the incubation. Objective 3: Outreach We updated our web-based N tool (https://extension.psu.edu/nitrogen-recommendations-for-corn) with the recalibrated equations developed in Objective 1. The new version of the website allows users to consider the economics of applying nitrogen fertilizer by entering the fertilizer cost and an expected price for corn. A dynamic graph illustrates how changes in the inputs affect the predicted fertilizer response curve and the N fertilizer recommendation. We met with our advisory board (virtually) in February to share research updates and plan for future events. We participated in several other outreach events during the second year of the project, including a farmer meeting organized by the Snyder County Conservation District (February), Penn State Extension's Organic Learning Circles (March) and Agronomic Diagnostic Clinic (July), and Penn State's Ag Progress Days (August). The summer extension workshops included hands-on activities such as using a Greenseeker NDVI sensor to estimate cover crop nitrogen content, hand soil texturing and the opportunity to try the web-based N tool on a laptop. The new calibration of the N tool (from Objective 1) was used to support four on-farm demonstration trials in Lancaster County, PA in 2023. Soil and cover crop data needed to calculate the N fertilizer recommendation were collected at each site. Six rates of N fertilizer were applied at sidedressing. The N tool accurately predicted the N fertilizer requirement at all four sites. Results from these demonstration trials were shared at field days and grower meetings throughout the summer of 2023 and will continue to be shared in coming months and years. Three undergraduate research projects completed this year will lay the foundation for future outreach products to support the N tool. One student used data from our research station to estimate the carbon credits that could be earned by planting cover crops and increasing soil organic matter levels. The net revenue that could be earned through two different carbon credit programs was adjusted for the costs of cover crop seeds and planting. This type of analysis can be used in outreach events/materials to illustrate the economic implications of growing cover crops, both in the short and long term. Another student project focused on the development of a cover crop look-up table to allow farmers to estimate the C:N ratio of winter-hardy cover crops instead of collecting plant samples and sending them to a laboratory for analysis. The analysis of nine years of data at the Penn State research station indicated that the spring C:N ratio of legume cover crops is less variable than non-legumes and that growing degree days (an index of cover crop maturity) can explain some of the variability in C:N of non-legumes and mixtures. A third student designed a hands-on activity demonstrating how we conduct laboratory incubation and measure soil microbial respiration using a LI-COR gas analyzer; this activity is intended for use at future farmer meetings. She also developed two fact sheets based on findings from our laboratory incubations and a template to report measured soil properties back to farmers who provided soil for incubation studies at Ursinus.
Publications
- Type:
Other
Status:
Published
Year Published:
2023
Citation:
White, C., Spargo, J. T., & Arrington, K., et al. (2023). Soil organic matter and cover crop-based nitrogen recommendations for corn. https://extension.psu.edu/soil-organic-matter-and-cover-crop-based-nitrogen-recommendations-for-corn.
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Progress 09/01/21 to 08/31/22
Outputs
Target Audience:Through several events over the past year, we shared the nitrogen (N) decision support tool with a diverse group of agricultural professionals in Pennsylvania. In March we held our annual meeting (virtually) with the farmers on our advisory board, who all grow organic corn, but are distributed geographically across Pennsylvania. We reached a broader group of Pennsylvania farmers/ agricultural professionals with several events, including a webinar in February (part of the Penn State Extension Series, "Making Cover Crops Pay") and two presentations at Penn State's Ag Progress Days in August. We extended our outreach beyond the agricultural community with a presentation at the Lancaster-Lebanon Watershed Forum and Science Symposium in Elizabethtown, PA (November 2021). The event brought together scientific researchers with watershed practitioners, including local government and community organizations. Our secondary target audience includes students with an interest in a career in organic agriculture. During the second year of the project, five undergraduate students at Penn State and three students at Ursinus either received academic credit or hourly wages for contributing to the research component of this project, including collecting soil and plant samples in the field and processing them for laboratory analyses. Students participated in fieldwork at the Penn State Research Station, the two on-farm research sites and other farms where soil samples were collected for the laboratory incubations. In addition to the group of students assisting with research, a broader student audience was reached through two guest lectures at Penn State. In October 2021, we gave a tour of the cover crops at the Penn State Research Station to students in an introductory agronomy course (Principles of Crop Management). Students were given an overview of how different cover crops affect soil nitrogen levels and a demonstration of using an handheld NDVI (Normalized Difference Vegetation Index) sensor to estimate the nitrogen content of cover crops. In March 2022, we presented a guest lecture to a group of upper-level undergraduates (Emerging Issues in Plant Sciences) about the nitrogen contributions of soil organic matter and cover crops, including a demonstration of the web-based nitrogen decision support tool. Changes/Problems:We have experienced delays in stable isotope analysis necessary for determination of microbial carbon use efficiency. The external labs we use to conduct these analyses experienced closures during the pandemic that have led to significant backlogs and extended turn around times. However, we are on track to send these samples in this fall. We postponed our plans for a spring or summer field day due challenges coordinating a time that project team members and farmers would be available to attend an in-person event. However, we did participate in other outreach activities, such as Penn State's Ag Progress Days. We have scheduled an in-person outreach event for December 2022. What opportunities for training and professional development has the project provided?The project has provided many opportunities for training and professional development. Eightundergraduate students participated in agricultural research through assisting with field and laboratory work for this project. Two graduate students, two laboratory technicians and a postdoctoral scholar helped to train and mentor the undergraduate students in their research experiences. We included these students in lab group meetings, which provided opportunities to listen to presentations by graduate students, discuss scientific journal articles, review draft manuscripts/presentations and view preliminary datasets that they had helped to collect. Two graduate students (PhD candidates) are each leading a component of this project as part of their dissertation research. Several project team members have had opportunities to attend conferences to present data collected as part of this project and learn about related research and outreach. How have the results been disseminated to communities of interest?We have continued to work with the farmers on our advisory board to guide the development of the N tool and the outreach component of this project. This partnership includes sharing data and farmer experiences to improve the tool and identify outreach opportunities. In March 2022, we met virtually with this team to share research updates and plan for farm visits and future outreach events. We shared the corn yield response to nitrogen additions at the research and on-farm sites and one farmer shared his experience with participating in the on-farm research. We presented results of nitrogen mineralized during laboratory incubations of soil samples collected from ten farms across Pennsylvania, including fields from some of the farmers in this group. We shared results of Greenseeker NDVI (Normalized Difference Vegetation Index) readings taken weekly at the research station in Spring 2021. This research was initiated in response to a question at the 2021 advisory board meeting: would readings taken early in the spring provide a reasonable estimate of the nitrogen content of cover crops at termination? Farmers were interested in this question because taking Greenseeker readings early in the spring would allow them more time to plan for manure and fertilizer applications. Also, we discussed interest among the farmers in having a member of our team visit their farm to collect soil samples to analyze for texture and organic matter (inputs to the N tool) and demonstrate taking NDVI readings with the Greenseeker. We followed up with two farms visits in the spring/summer 2022. Finally, we discussed plans for future outreach events to share the N tool with broader audiences. Farmers recommended seeking out opportunities to participate in existing outreach events in addition to planning our own field day. Members of our project team have given twelve presentations in which the N decision support tool was introduced to agricultural professionals, researchers, students and others. These presentations were given at two webinars, four conferences, four extension events and two guest lectures. A complete list of events is provided in the Other Products/Outputs section. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: Agronomic validation: The second year of data collection will be completed with the corn harvest in October/November 2022, when we will measure yield and the N content of the corn ears for the six N levels of each plot at the research station and on-farm sites. These data combined with the plot-specific cover crop biomass nitrogen and soil properties will be used to test the recalibrated model's ability to predict unfertilized corn yield using an independent dataset. In addition, the corn yield data for the N addition subplots will allow us to assess the accuracy of the N tool fertilizer recommendations. Objective 2: Biogeochemical validation We will complete the processing of the soil samples from the eleven farms and the research station to determine the C:N ratio of these soils and their microbial biomass. Our expected outcome from this analysis is that C:N ratio of soil and microbial are similar (~10), validating this assumption in the biogeochemical equations underlying the N decision support tool. We will complete the estimations of microbial carbon use efficiency using the 18O tracer technique to determine the humification efficiency (ε) for the soil samples collected from the eleven farms and the research station. These results will allow us to evaluate the relationship between soil texture and humification efficiency in the biogeochemical equations. We will complete the soil and soil extract analyses for the manipulated texture experiment. This will complete our dataset and from there we can begin data analysis to assess trends in Carbon Use Efficiency and humification efficiency as a function of soil texture. We will evaluate the recalibrated model's calculated values for humification efficiency by comparing them to measured values the laboratory incubation studies and also from a litter bag decomposition study in the field (at the research station). Objective 3: Outreach We have scheduled a winter extension meeting for December 16, 2022 in New Columbia, PA. This will be a half-day outreach event with a focus on the nitrogen decision support tool. We will provide a short introduction to the concepts of the fertility of soil organic matter and cover crops, an overview of the field and laboratory research components of the project and a demonstration of using the web-based version of the tool. The remainder of the time will be used for hands-on activities, including using the Web soil survey to estimate soil texture, hand texturing soil samples, taking Greenseeker NDVI readings to estimate the nitrogen content of cover crops, and testing scenarios using the web-based N tool. The event will also allow time for questions about farmers sharing experiences with planting cover crops and nitrogen management for corn. We will complete the cover crop look-up table, which will allow farmers to use the tool without having to collect cover crop samples to determine their spring C:N ratio. Also, as an alternative to collecting NDVI readings in the field, the table will provide estimates of the nitrogen content of cover crops in the spring. The estimates in the look-up table will be developed from a dataset of cover crop samples collected from sites across Pennsylvania as part of over a decade of research at Penn State. The look-up table will include a wide variety of cover crop species and mixtures, with subgroups for plant maturity and legume percentage for mixtures.
Impacts
What was accomplished under these goals?
We are working to refine the nitrogen (N) decision support tool in several ways, including expanding the range of sites for which it is valid, testing the underlying assumptions and providing ways to estimate the inputs required to run the tool. The original equations used to calculate fertilizer recommendations were developed for fine to medium-textured soils. We have expanded our input dataset to include sites with coarser-textured soils and recalculated the equations. We are testing the new equations with both field and laboratory experiments to make sure that the underlying assumptions are valid and that their recommendations are appropriate for a broad range of sites. In addition, we are developing outreach materials to facilitate the use of the tool. One example is the cover crop look-up table, which will allow farmers to estimate the cover crop information needed to run the tool without having to collect samples or take readings in the field. Objective 1: Agronomic Validation We completed the first year of the agronomic validation with the corn harvests at the on-farm sites and research station in October/November 2021. At both the on-farm sites and the research station, 2021 was a high-yield year for corn. However, the N tool predictions for unfertilized corn yield did not correspond well with measured yields. In part, this was due to the fact that some of the plots at the research station and the fields at one of the on-farm sites had coarser-textured soils than the sites used to develop the equations for predicting unfertilized corn yield. We recalibrated the equations underlying the tool with sites covering a broader range of soil textures. The expanded calibration dataset included data collected from previous research projects along with the on-farm research sites, resulting in nearly triple the number of observations used to calibrate the original equations. Overall, the new model structure resulted in better correspondence between predicted and measured unfertilized corn yield. We used the 2021 research station data as an independent test dataset for the recalibrated equations. Generally, the new model structure resulted in lower predicted yields compared to the original model structure. The lower predicted yields were tied to higher model-calculated values for humification efficiency, a parameter indicating the degree to which soil microbes retain nitrogen in their biomass. We began the second year of the agronomic validation with planting cover crops (August 2021) at different fields for the on-farm sites and a different entry at the research station. We used the same methods as year 1, including collecting cover crop biomass samples and measurements of NDVI (Normalized Difference Vegetation Index) in fall and spring. Four to fiveweeks after corn planting at the research station and the on-farm sites, plots were divided into six subplots for nitrogen additions. We used the same methods as year 1 for the N additions, except for replacing the highest rate (240 pounds per acre) with a lower rate (90 pounds per acre) at the research station. This change was based on the limited yield response to the higher nitrogen levels for many research station plots in year 1. Objective 2: Biogeochemical Validation We completed laboratory incubations of soil samples from sixteen agricultural fields collected from four different commercial farms and the Penn State research station in spring 2021. In addition to estimating nitrogen mineralization from five cover crop residues, we also determined initial microbial biomass carbon and nitrogen, microbial community structure (via phospholipid fatty acid analysis), microbial carbon use efficiency, carbon nitrogen concentration, and soil texture. Carbon use efficiency is a proxy for humification efficiency (ε) that we measure as the quantity of 18O incorporated into microbial DNA. In spring 2022, we collected additional soil samples from 20 fields on seven commercial farms and the research station. These samples have undergone the same incubation protocols used in 2021 to determine nitrogen mineralization parameters and have been processed to determine initial microbial and soil properties. Data from 2021 and 2022 will be analyzed to assess correlations between nitrogen mineralization and soil properties. These analyses will inform modifications to the biogeochemical equations used in the N decision support tool. In addition, microbial carbon use efficiency data will be used to determine whether soil texture is a predictor of humification efficiency (ε). We continued with our manipulative texture experiment by completing a 3-month laboratory incubation to evaluate the influence of soil texture on Carbon Use Efficiency and humification efficiency. Soils from the research station represent a range of ~17-47% sand, so we used these soils for the incubation. Additionally, we constructed soils to represent a wider texture gradient, ~17-59% sand, which expands our ability to calibrate the tool to be effective for sites with greater sand content than what is present at our PSU field site. We used13C-glucose tracing methods to track the fate of added carbon through the pools of microbial biomass and soil organic matter and can then compare the13C accumulated in these pools to the13C lost as CO2. As a result of the incubation, we collected over 250 gas samples, which were analyzed for13C-CO2. We also collected over 250 soil samples, which will be analyzed for the13C content of soil organic matter, and over 500 soil extracts, which will be analyzed for the13C content of microbial biomass. Objective 3: Outreach We met with our advisory board (virtually) in March to share research updates and plan for future events. Research updates included the role of soil texture in nitrogen mineralization (from laboratory incubations), how Greenseeker NDVI readings change over time and yield response to nitrogen at the on-farm research sites. In addition, one farmer shared his experiences with participating in the on-farm trials. We discussed content/timing for an in-person outreach event. We participated in several other outreach events during the second year of the project. In February, we developed a webinar as part of Penn State Extension's "Making Cover Crops Pay" series. Content focused on the effects of cover crops at three time scales: reducing nitrogen leaching while they are growing (fall through spring), influencing nitrogen mineralization during the corn growing season and building long-term soil fertility by increasing organic matter. Also, we showed how different cover crops affect the corn yield response to nitrogen additions (based on data from the research station and on-farm sites) and demonstrated the nitrogen decision support tool. In August, we gave two presentations at Penn State's Ag Progress days. We shared several scenarios with the audience to compare how cover crops and soil organic matter affect the tool's nitrogen fertilizer recommendations. Also, we showed participants how to use the Greenseeker NDVI sensor on the cover crop demonstration plots. In November 2021, we gave a presentation Lancaster-Lebanon Watershed Forum and Science Symposium in Elizabethtown, PA. Here we focused on the effectiveness of cover crops to reduce nitrogen leaching based on nine years of data at the Penn State research station. We began working on a new outreach product to complement the N tool: the cover crop look-up table. The table will provide estimates of the carbon to nitrogen ratio (C:N) of different cover crop species and mixtures, which will allow farmers to use the tool without having to collect cover crops samples for laboratory analysis. We created a preliminary version of the look-up table by summarizing the cover crop biomass data collected at the Penn State research station since 2012.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Zhang, Z, J.P. Kaye, B.A. Bradley, J.P. Amsili, V. Suseela. 2022. Cover crop functional types differentially alter the content and composition of soil organic carbon in particulate and mineral-associated fractions. Global Change Biology 28: 5831-5848. DOI: 10.1111/gcb.16296
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Progress 09/01/20 to 08/31/21
Outputs
Target Audience:The goal of our project is to develop a nitrogen (N) decision support tool suitable for calculating the N fertilizer requirement for any corn field in Pennsylvania. Accordingly, Pennsylvania farmers growing corn are our primary target audience. In addition, consultants, extension educators and any agricultural professionals who assist with making N fertilizer decisions would be included among those we seek to reach through our outreach activities. Through presentations/posters at a number of events/webinars over the past year, we introduced the N decision support tool to a diverse group of agriculture professionals in Pennsylvania and other states as well as members of environmental/conservation NGOs and regulatory agencies. To help us test and improve the N tool, we held a meeting (virtually) with our advisory board to vet ideas regarding the visualization, inputs, and outputs of the web dashboard interface. We have worked closely with many of the farmers in this group through previous research and extension activities and they have indicated a strong interest in continuing to work with us to develop our N decision support tool. These farmers are distributed geographically across Pennsylvania and southern New York. Several are involved with farmer network groups (including the Central Susquehanna Valley Organic Crop Producers Network and PASA Sustainable Agriculture) and a few have offered to host educational events for this project at their farms. Thus, we are optimistic that the strong interest of our advisory board in the N decision support tool along with their connections with other farmers will help us to expand our target audience in the second and third year of the project. Our secondary target audience includes students with an interest in a career in organic agriculture. During the first year of the project, three undergraduate students at Penn State and two students at Ursinus either received academic credit or hourly wages for contributing to the research component of this project through assisting with activities in the laboratory and the field, including the research station and commercial farms. Penn State students were given a demonstration of the web-based N tool (similar to the one at our advisory board meeting) to show how farmers could use the tool to account for the N contributions of soil organic matter and decomposing cover crops. Changes/Problems:Our outreach approach was impacted by safety concerns about in-person events during the pandemic. Therefore, we decided not hold a field day as planned for the first year of the project. Still, we were able to hold a meeting over Zoom with our advisory board members and participate in several conferences virtually. We plan to have a field day in spring 2022. What opportunities for training and professional development has the project provided?The project provided opportunities for five undergraduate students to participate in agricultural research through assisting with field and laboratory work. We included undergraduate students in lab group meetings which provided opportunities to listen to presentations by graduate students, discuss scientific journal articles and ask questions about attending graduate school. The Agroecology Lab at Ursinus College, guided by Dr. Denise Finney provided a workshop for two graduate students to learn how to measure microbial carbon use efficiency using the oxygen-based stable isotope technique. The workshop applied the method of learning by doing and covering the process from sample management, calculating the amounts of labeled water additions, running the incubations, doing microbial carbon biomass and microbial respiration. Several project team members attended scientific conferences where the N decision support tool project was represented, including the PASA 2021 Virtual Sustainable Agriculture Conference, where we contributed a presentation and the Northeast Cover Crops Council 2021 Virtual Conference, where we contributed a poster. How have the results been disseminated to communities of interest?In March 2021, we met with our advisory board (via Zoom) and shared two versions of our N decision support tool: 1) the current version on the Penn State Extension website (https://extension.psu.edu/graphical-analysis-tool) and the pilot "Economic Optimum" version, which considers how the cost of nitrogen fertilizer can reduce the optimum application rate. Farmers at the meeting tested both versions of the tool using site-specific soil and cover crop information from their farms (collected through previous on-farm research projects). There was a strong interest in the web-based N decision support tool, especially the Economic Optimum version. Advisory board members posed questions and offered suggestions and we will use this feedback to guide the development of future versions of the tool. Members of our project team have presented 9 talks or posters at outreach events in which the N decision support tool was introduced to agricultural professionals in Pennsylvania and other states. We contributed to the PASA 2021 Virtual Sustainable Agriculture Conference with a recorded presentation about the principles that regulate N availability from cover crops and soil organic matter along with a demonstration of our web-based N decision support tool. This annual event typically brings together over 2,000 growers, buyers, distributors and consumers. A complete list of events is provided in the Other Products/Outputs section. What do you plan to do during the next reporting period to accomplish the goals?Objective 1: Agronomic validation: The first year of data collection will be completed with the corn harvest in October 2021, when we will measure yield and the N content of the corn ears for the six standard N levels of each plot at the research station and on-farm sites. These data combined with the plot-specific cover crop biomass nitrogen and soil properties will be used to conduct our first test of the model's ability to predict unfertilized corn yield using an independent dataset. In addition, the corn yield data for the N addition subplots will allow us to assess the accuracy of the N tool fertilizer recommendations. This August we initiated our second year of the agronomic validation with the planting of cover crops in a different entry of the research station and different fields at the on-farm sites. We will repeat the methods from the first year of the project, including collecting NDVI readings, sampling and analyzing cover crop biomass, planting corn following cover crop termination and applying Chilean nitrate at six standard N addition levels in the corn plots. Objective 2: Biogeochemical validation We will complete the processing of the soil samples from the ten farms and the research station to determine the C:N ratio of these soils and their microbial biomass. Our expected outcome from this analysis is that C:N ratio of soil and microbial are similar (~10), validating this assumption in the biogeochemical equations underlying the N decision support tool. We will complete the incubations using 13C-glucose tracing to determine the humification efficiency (ε) for the soil samples collected from the ten farms and the research station. These results will allow us to evaluate the relationship between soil texture and humification efficiency in the biogeochemical equations. We will complete the incubations using 13C-glucose tracing to determine the humification efficiency (ε) for the soils in the manipulated texture gradient and the inherent texture gradient at the research station. These results will allow us to isolate the effect of texture on ε by reducing the variability in other factors among the soil samples from the commercial farms. They will create a second independent dataset to test the biogeochemical equations. Objective 3: Outreach We will use the feedback from our first advisory board meeting to incorporate more guidance for users to enter inputs to the tool and interpret the results. Links will be added next to each input to help users collect or estimate the site-specific information needed to use the tool. In addition, more information will be added to help users interpret the outputs of tool, including explanations of concepts such as yield gap, microbial carbon use efficiency and fertilizer efficiency. Limitations of the tool will be mentioned, such as the fact that we are still testing and refining the tool for use on coarser-textured soils (those with greater than 50% sand) and that fertilizer recommendations are based on average-year temperatures and precipitation in Pennsylvania. We will hold a second advisory board meeting in spring 2022 share the next version of the web-based N tool. We will work with extension educators to make sure that the farmers on our advisory board have the opportunity to use the Greenseeker NDVI sensor to estimate the nitrogen content of cover crop biomass on their fields to use as inputs to the N tool. We will work with these farmers to use the N tool for fields that will be planted in corn to compare the the N recommendations calculated by our decision support tool to the N application rates they would determine through other tools or methods. We will hold a field day in spring 2022 to test the N decision support tool with farmers beyond those on our advisory board. Field day attendees will engage in participatory activities such as assessing cover crop biomass and N content with the NDVI meter, using the decision support tool to estimate fertilizer or manure input needs, and calculating impacts of fertilizer choices on economic returns. We will continue to participate in conferences and other outreach events to introduce the N decision support tool to agricultural professionals in Pennsylvania and other states. For example, we will be presenting at the Keystone Crop and Soils Conference in October 2021. Attending this meeting will be approximately 150-200 Certified Crop Advisors, who will be earning their continuing education credits in several subjects, including nutrient management.
Impacts
What was accomplished under these goals?
One of the challenges that farmers face is deciding which fertilizers to use and how much to apply to their fields. Nitrogen is an important plant nutrient and component of most fertilizers. Too little nitrogen stunts crop growth, but too much can cause excessive weed pressure and nitrogen losses to the environment. Excess nitrogen in drinking water can make it unsafe for human consumption and excess nitrogen in lakes and streams can be detrimental to aquatic life and human recreation. Although most crops require supplemental nitrogen for optimum growth, some nitrogen can be supplied from the soil and decomposing plant residues, including cover crops, which some farmers grow from fall to spring. We have developed an online tool that predicts corn yield based on the amount of nitrogen that is slowly released from the soil and decomposing cover crops. Using site-specific information, the tool calculates the amount of nitrogen needed to supplement the existing soil fertility and to achieve a goal for corn yield. Through this project we are working to test our tool on corn fields across Pennsylvania with a wide variety of cover crops and soil types. We are testing the accuracy of the equations for predicting corn yield with field research at three sites: our research station in central Pennsylvania and two commercial farms in northeastern and southeastern PA. These experiments involve applying nitrogen fertilizer at six rates at each site or plot to test the accuracy of the fertilizer recommendations calculated by the tool. Also, we are performing laboratory experiments using a wide variety of soil samples collected from 11 farms across Pennsylvania and southern New York. This research will expand our understanding of how soil microbes slowly release nitrogen, making it available to plants. Finally, we are working with a small group of farmers to test the tool on their fields to make sure it is easy to use and helpful in making fertilizer decisions. We are developing a new version of the tool that allows users to compare how the cost of different nitrogen fertilizer options affects the recommended application rates, allowing farmers to consider profitability in their decision-making. Ultimately our goal is to provide a nitrogen decision support tool that assists farmers in choosing the optimal type and amount of fertilizer for their fields, maximizing profitability for farmers and improving environmental quality. Objective 1: Agronomic Validation We accomplished the proposed tasks for the first year of the agronomic validation with a few changes to the methodology. Cover crops were planted at both the research station and the two on-farm sites, but at the on-farm sites farmers selected the cover crops, rather than using the three cover crop types we had planned (grass, legume and grass/legume mix). Both farmers selected winter-hardy cover crops, so we did not sample fall biomass at the on-farm sites because this information was not needed to test the N decision support tool. At both the research station and the on-farm sites, we sampled cover crop biomass in all plots just before termination to calculate the cover crop nitrogen content and C:N ratio. Also, we took NDVI readings at all biomass sampling locations to test the equations we are using to estimate cover crop nitrogen content in the N decision support tool. At the research station and the on-farm sites, plots were divided into six subplots, with standard nitrogen addition levels 0, 30, 60, 120, 180, and 240 pounds N per acre. We used the six standard N rates (rather than the four variable rates based on N tool recommendations indicated in the proposal) because the two additional N levels will allow us to better determine the shape of the yield response curve for each site and cover crop type. Also, the six standard levels will allow us to compare the response curves of different sites and cover crop types with a consistent methodology. Finally, exposing the corn to a wide range of N availability will identify cases in which the N tool fertilizer recommendation were incorrect, allowing us to recalibrate the model if needed. At both the research station and the on-farm sites, we used Chilean nitrate for the N additions (rather than feather meal) because it has a more defined availability (100%) and is easier to handle for research purposes. Objective 2: Biogeochemical Validation In spring 2021, we collected soil samples from 11 locations (ten commercial farms and the research station) for the biogeochemical validation of the model underlying the N decision support tool. The soil samples were collected from locations across Pennsylvania and southern New York and they cover a wide range of soil textures and organic matter levels. We are processing these samples to determine the C:N ratio of both the soil and the microbial biomass. Also, we began the incubations of these soil samples using 13C-glucose tracing to determine whether variation humification efficiency (ε) across farms is correlated with soil texture. While these soil samples cover a wide range of textures, they may differ in other ways that affect ε, such as differences in soil microbial communities. To isolate the effect of soil texture of on ε, we began our manipulative texture experiment. We developed the technique to sieve sand from a fine-textured soil sampled from a single plot at the research station and then incrementally add the sieved sand to the original fine-textured soil to create a manipulated gradient of soil textures. The manipulated gradient includes the existing range of textures at the research site and a sandier soil similar the coarser-textured soils at one of the on-farm sites. We did one pilot incubation with these soils using a single type of cover crop litter (C:N~20) to determine the appropriate moisture level to use for future incubations. Objective 3: Outreach Our primary outreach goal for the first year of the project was to share the N decision support tool with farmers and solicit their feedback regarding the visualization, inputs, and outputs of the web dashboard interface. In March 2021, we hosted a Zoom meeting with our advisory board members, where we shared two versions of our N decision support tool with this group: 1) the current version on the Penn State Extension website (https://extension.psu.edu/graphical-analysis-tool) and the pilot "Economic Optimum" version, which considers how the cost of nitrogen fertilizer can reduce the optimum application rate. Since we had worked with these farmers in the past, we were able to compile site-specific soil and cover crop information for each farmer to enter as inputs to the tool. The farmers who tested the N decision support tool provided valuable feedback to guide the development of future versions of the tool. They pointed out that not all farmers will have site-specific soil texture information for their fields, so it would be helpful to add a feature to help users to estimate soil texture. Our test users were very interested in using the Greenseeker NDVI sensor to estimate the nitrogen content of cover crop biomass, but they had a number of questions including where to borrow a Greenseeker sensor and when NDVI readings could be taken. These questions will help us to incorporate more guidance into the tool. In addition, they led us to investigate how NDVI readings changed over the spring growing season for different cover crops grown at the research station, which will help us to develop new guidance for when to take NDVI readings. Finally, our advisory board members provided a number of ideas for ways to expand the scope of the tool, such as the incorporating the ability to consider how manure applications would affect other nutrients besides nitrogen and how fertilizer recommendations could be adapted for year-to-year weather variability.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
White CM, Finney DM, Kemanian AR, Kaye JP. Modeling the contributions of nitrogen mineralization to yield of corn. Agronomy Journal. 2020;114. https://doi.org/10.1002/agj2.2047
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