Progress 04/01/17 to 03/31/20
Outputs
Target Audience:?Maize research community and the agricultural community Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Two postdoctoral research associates and three PhD graduate students in electrical engineering received training to design, manufacture, test and optimize miniature sensor. The postdoctoral research associates also received training in the collection of in-season data, performed growth analysis, and calibrated crop growth models and soil sensors. A postdoc research associate (part time) from Agronomy received training in field data collection/analysis during the season, and calibration of the ASPIM maize model for different genotypes. A graduate student in Genetics received training in data collection and analyses through these sensors. How have the results been disseminated to communities of interest?The results have been disseminated through workshop, seminars and posters at 6 domestic and international conferences, and published in a scientific journal. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
Overall impact statement: We have successfully manufactured, calibrated, validated and optimized soil and in planta nitrate sensors of reliable accuracy to facilitate the implementation and utility of these sensors in agricultural settings. We were able to integrate nitrate and genotypic data into the APSIM model and will be able to use the enhanced model for prediction. This research enhances our understanding of and ability to predict yield and Genotype x Environment interactions for the breeding of crops that exhibit increased yields and yield stability. Objective 1... Calibrate existing soil and in planta nitrate sensors. In years 2017-2019, we successfully manufactured about two hundred soil and in planta nitrate sensors and their data loggers in a laboratory setting. We also calibrated and validated soil and in planta nitrate sensors by making comparisons against conventional nitrate measurements in both laboratory and field settings. We calibrated the sensors across ranges of plant and soil nitrate concentrations that are relevant to agricultural decision-making. We used the verified in planta nitrate data to develop relationships between corn stalk nitrate concentrations and plant N sufficiency. Our initial work demonstrates that the relationship between stalk nitrate concentration and N sufficiency is similar to existing plant and soil tests for N sufficiency. For the soil sensors, we developed protocols to scale the new sensor measurements, which measure soil solution nitrate concentration, to conventional soil tests that measure salt-extractable soil nitrate concentrations. In the field, agreement between the two methods after scaling was excellent (R = 0.76). In the lab, the two measurements were consistently within 5% of each other. Using the verified continuous soil nitrate sensor data, we determined optimum sampling frequencies to capture accurate estimations of daily mean soil nitrate concentration. These data and analyses will speed the implementation and utility of the plant and soil nitrate sensors. Objective 2... Integrate nitrate and genotypic data into an existing crop model. In years 2017 and 2018 we set up experiments in which we collected phenotypic data for 12 genotypes each year. The data included phenological, morphological, physiological and chemical composition data per genotype. Many phenotypes per genotype were collected each experiment/year. Years 2017 and 2018 were relatively dry and wet, respectively. Following data collection and analysis, we incorporated all the data into the Agricultural production Systems Simulator (APSIM model) to initiate calibration of genotype-specific parameters. The process was interactive, starting with phenological parameters and ending with grain component and quality parameters following standard calibration procedures. The calibration process ceased when a good fit between observed and simulated data was achieved, judged by the root mean square error. In years 2017 and 2019 we incorporated soil nitrate and sensor-based data into the APSIM model. It was in-season time series data from about 9 plots per year. The trends between APSIM, sensor and measured were very good. Objective 3... Test predictive ability of genotype-enable crop model. To test model predictions accuracy, we used the validation/testing data from the year 2018. Overall, the model worked well in simulating more than 10 phenotypes (including yields) for the 12 genotypes used by the G2F network. Impacts - key results from modeling In this project we collected many phenotypic data, which is not typical in breeding programs. This dataset allowed us to identify which measurements (phenotypes) are the most important to characterize a genotype in the model. We found that by using only phenological observations (nondestructive and quickly obtained) model accuracy doubled from the default without any measurements. By including growth-related phenotypes (which requires destructive sampling and is laborious), model accuracy increased four-fold over the default. Among the many phenotypes (grain protein, grain yield, etc.) the simulated error ranged from 7 to 18% relative root mean square. Model parameter sensitivity analysis indicated that the top three most sensitive parameters related to grain yield are: cardinal temperatures, radiation use efficiency and potential kernel number per ear among 20 parameters tested. Soil N was extremely useful during calibrations for two reasons. First, it helped calibrate more accurate plant traits related to N uptake and N remobilization. Second, it helped to more accurately set up the contribution of inorganic nitrogen from the soil organic matter.
Publications
- Type:
Theses/Dissertations
Status:
Published
Year Published:
2019
Citation:
Xinran Wang, Next-generation reference electrodes with high potential stability towards long-term sensor measurements; Iowa State University, Ph.D. Dissertation, 2019.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Ali, M.A., Wang, X., Chen, Y., Jiao, Y., Mahal, N.K., Moru, S., Castellano, M.J., Schnable, J.C., Schnable, P.S. and Dong, L., 2019. Continuous Monitoring of Soil Nitrate Using a Miniature Sensor with Poly (3-octyl-thiophene) and Molybdenum Disulfide Nanocomposite. ACS applied materials & interfaces, 11(32), pp.29195-29206.
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Progress 04/01/18 to 03/31/19
Outputs
Target Audience:Maize research community and the agricultural community Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Three engineering Ph.D. students were partially supported by this project. They received training to design, manufacture, and test miniature sensors for agriculture. A part time postdoc were partially supported by this project also. He received training to collect in-season data, perform growth analysis, and calibrate crop growth models and soil sensors. How have the results been disseminated to communities of interest? American Seed Trade Association, Plant Breeding Innovation, Chicago IL, 6 December 2018. "Leveraging Precision Phenotyping in Genetic Evaluation" Liang Dong. Miniature sensors for soil and plant analytics,The International Conference on Nano/Micro Engineered and Molecular Systems, April 22-26, 2018, Singapore. Liang Dong. Graphene Foam Based Biochemical Sensors and Energy Harvesting Devices, IEEE Nanotechnology Materials and Devices Conference, Oct. 14-18, 2018, Portland, OR. Liang Dong. Nitrate sensors for plants and soils, 2018 ASA and CSSA Meeting, Nov. 4-7, Baltimore, MD. Liang Dong. Plant electronic sensors, Stewards of the Future Workshop, Dec. 2-4, Raleigh, NC. What do you plan to do during the next reporting period to accomplish the goals?We grew 12 genotypes that are included in the Genomes 2 Fields project. For each genotype, we measured crop growth and soil dynamics at high resolution throughout the growing season. In Year 3, we will use these data to test the predictive ability of genotype-enabled cropping systems models.
Impacts
What was accomplished under these goals?
Overall impact statement: We calibrated the model and the resulting simulation data were accurate for biomass production, yield and yield components. We also made progress in translating nitrate sensor data so they are suitable as model data. Objective 1... Calibrate existing soil and in planta nitrate sensors. Nothing to report for this period. Objective 2... Integrate nitrate and genotypic data into an existing crop model. Crop and soil N dynamics differed with genotype. Although the nitrate concentrations measured by the sensors were accurate, they require significant model calibration to be useful. All cropping systems models have been validated and calibrated based on salt-based extraction of soil nitrate. The sensors provide a new and potentially more realistic measurement of plant-available nitrate because they measure soil solution nitrate concentration. However, these new data require new model calibrations. In year 2 we continued evaluating the 12 hybrids for yield and other measurements. We measured periodically over the growing season several plant traits that include biomass production, biomass partitioning to different tissues, tissue nitrogen and carbon concentrations, and leaf are index. These data are currently processed with the APSIM model and will serve as a validation dataset to previous year's observations. It should be noted that in year 2018 we used two environments and 12 genotypes within an environment. Also, in year 2 we calibrated the model using data from year 1. The model simulated biomass production, yield and yield components accurately for all the genotypes. From this exercise we derived a list of genotype-specific parameters. In addition, we made progress in translating the nitrate sensor data to a model-relevant input. We did this through extensive comparisons of conventional salt-extract soil nitrate measurements and new sensor nitrate measurements. Most promising was that the temporal pattern of nitrate concentration fluctuation was highly similar between the two methods. Objective 3... Test predictive ability of genotype-enable crop model. Nothing to report for this period.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2018
Citation:
Liang Dong. Graphene Foam Based Biochemical Sensors and Energy Harvesting Devices, IEEE Nanotechnology Materials and Devices Conference, Oct. 14-18, 2018, Portland, OR.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2018
Citation:
Liang Dong. Nitrate sensors for plants and soils, 2018 ASA and CSSA Meeting, Nov. 4-7, Baltimore, MD.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2018
Citation:
Liang Dong. Plant electronic sensors, Stewards of the Future Workshop, Dec. 2-4, 2018, Raleigh, NC.
- Type:
Theses/Dissertations
Status:
Accepted
Year Published:
2019
Citation:
Yueyi Jiao. Wearable Sensors for Structural Health Monitoring and Agricultural Nutrient Management. Ph.D. thesis. 2019.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2018
Citation:
Liang Dong. Miniature sensors for soil and plant analytics, The International Conference on Nano/Micro Engineered and Molecular Systems, April 22-26, 2018, Singapore.
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Progress 04/01/17 to 03/31/18
Outputs
Target Audience:Maize research community and the agricultural community. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided?Three engineering Ph.D. students were partially supported by this project. They received training to design, manufacture, and test miniature sensors for agriculture. 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 grew 12 genotypes that are included in the Genomes 2 Fields project. For each genotype, we measured crop growth and soil dynamics at high resolution throughout the growing season. In Years 2 and 3, we will use these accomplishments to test the predictive ability of genotype-enabled cropping systems models.
Impacts
What was accomplished under these goals?
IMPACT: We demonstrated that the new, low-cost plant and soil nitrate sensors measure nitrate with accuracy and precision that is equal to or better than conventional measurements. This is key to the future of plant nutrition and soil fertility. Objective 1... Calibrate existing soil and in planta nitrate sensors. Hybrids were generated and grown in year 1. We calibrated our existing sensors to measure nitrate concentrations in both the soil and in planta. The nitrate sensors use field effect transistors and microelectrodes modified by our ion-selective membrane. The calibration temperature of the sensor ranged from 5 to 35 degrees Celsius, over which the sensor signal drifted ~9.4%, which can be compensated by our readout circuitry of the sensor. The reference electrode of the sensor exhibited 2.8%, 5.2%, 6.4%, 7.7%, and 8.5% shift over a duration of 47 days at room temperature (23 degrees Celsius) in the standard chloride solutions of 5000, 1000, 200, 50, and 1 ppm, respectively. The sensor also showed 7.3%, 6.8%, 4.5%, 2.2%, and 5.6% relative standard deviation in response to sulfate (500 ppm), phosphate (500 pm), potassium (500 ppm), chloride (500 ppm), and bicarbonate (500 ppm), respectively, with 20 ppm nitrate in the test solution. The dynamic range of the sensor was found to be from ~1 ppm to ~4000 ppm nitrate. Currently, we are setting up a measurement system consisting of a number of nitrate sensors. The fabrication processes for the sensors were improved compared to our previous ones. In particular, we applied a robotic dispensing system to deposit and pattern five key coatings (i.e., hydrophobic conducting layer, ion-selective membrane, sodium chloride containing polymer, and proton exchange membrane) for nitrate sensors on a wafer scale. In addition, we developed an electronic readout circuit for each sensor. Each circuit consists of a signal amplifier, a filter, an analog-to-digital converter, a data storage SD card, a microcontroller, and a rechargeable battery. Currently, we are building a platform that consists of a number of sensors and readout circuits for field measurement. Objective 2... Integrate nitrate and genotypic data into an existing crop model. Crop and soil N dynamics differed with genotype. Although the nitrate concentrations measured by the sensors were accurate, they require significant model calibration to be useful. All cropping systems models have been validated and calibrated based on salt-based extraction of soil nitrate. The sensors provide a new and potentially more realistic measurement of plant-available nitrate. However, these new data require new model calibrations. Because of Year 1, we have planted the same 12 genotypes and are continuing nitrate sensing. Additional hybrids are generated for growing in year 2. Objective 3... Test predictive ability of genotype-enable crop model. Nothing to report for this period.
Publications
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