TY - JOUR
T1 - Development of a data-assimilation system to forecast agricultural systems
T2 - A case study of constraining soil water and soil nitrogen dynamics in the APSIM model
AU - Kivi, Marissa S.
AU - Blakely, Bethany
AU - Masters, Michael
AU - Bernacchi, Carl J.
AU - Miguez, Fernando E.
AU - Dokoohaki, Hamze
N1 - Additionally, we wanted to acknowledge those funding sources that supported the work of the Energy Farm team. First, the data used in this study was funded in part by (1) the Leverhulme Centre for Climate Change Mitigation, funded by the Leverhulme Trust through a Research Centre award ( RC-2015-029 ), (2) the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) at the University of Illinois, and (3) the Global Change and Photosynthesis Research Unit of the USDA Agricultural Research Service . Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Agriculture (USDA). Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. USDA is an equal opportunity provider and employer.
The authors would like to thank all those on the Energy Farm team who made the presented case study possible. In particular, we would like to thank Grace Andrews and Heather Goring-Harford, who performed the chemical analyses for the nitrate leaching data, and Konrad Taube and Haley Ware, who helped with water collection and water filtering in 2018 and 2019. We also want to thank Caitlin Moore and Evan Dracup, who helped to collect and process much of the other data from the plot. Additionally, we wanted to acknowledge those funding sources that supported the work of the Energy Farm team. First, the data used in this study was funded in part by (1) the Leverhulme Centre for Climate Change Mitigation, funded by the Leverhulme Trust through a Research Centre award (RC-2015-029), (2) the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) at the University of Illinois, and (3) the Global Change and Photosynthesis Research Unit of the USDA Agricultural Research Service. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Agriculture (USDA). Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. USDA is an equal opportunity provider and employer.
PY - 2022/5/10
Y1 - 2022/5/10
N2 - As we face today's large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of our forecasts remains limited by current tools and methods. To overcome the limitations of process-based models and observed data, we iteratively designed and tested a generalizable and robust data-assimilation system that systematically constrains state variables in the APSIM model to improve forecast accuracy and precision. Our final novel system utilizes the Ensemble Kalman Filter to constrain model states and update model parameters at observed time steps and incorporates an algorithm that improves system performance through the joint estimation of system error matrices. We tested this system at the Energy Farm, a well-monitored research site in central Illinois, where we assimilated observed in situ soil moisture at daily time steps for two years and evaluated how assimilation impacted model forecasts of soil moisture, yield, leaf area index, tile flow, and nitrate leaching by comparing estimates with in situ observations. The system improved the accuracy and precision of soil moisture estimates for the assimilation layers by an average of 42% and 48%, respectively, when compared to the free model. Such improvements led to changes in the model's soil water and nitrogen processes and, on average, increased accuracy in forecasts of annual tile flow by 43% and annual nitrate loads by 10%. Forecasts of aboveground measures did not dramatically change with assimilation, a fact which highlights the limited potential of soil moisture as a constraint for a site with no water stress. Extending the scope of previous work, our results demonstrate the power of data assimilation to constrain important model estimates beyond the assimilated state variable, such as nitrate leaching. Replication of this study is necessary to further define the limitations and opportunities of the developed system.
AB - As we face today's large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of our forecasts remains limited by current tools and methods. To overcome the limitations of process-based models and observed data, we iteratively designed and tested a generalizable and robust data-assimilation system that systematically constrains state variables in the APSIM model to improve forecast accuracy and precision. Our final novel system utilizes the Ensemble Kalman Filter to constrain model states and update model parameters at observed time steps and incorporates an algorithm that improves system performance through the joint estimation of system error matrices. We tested this system at the Energy Farm, a well-monitored research site in central Illinois, where we assimilated observed in situ soil moisture at daily time steps for two years and evaluated how assimilation impacted model forecasts of soil moisture, yield, leaf area index, tile flow, and nitrate leaching by comparing estimates with in situ observations. The system improved the accuracy and precision of soil moisture estimates for the assimilation layers by an average of 42% and 48%, respectively, when compared to the free model. Such improvements led to changes in the model's soil water and nitrogen processes and, on average, increased accuracy in forecasts of annual tile flow by 43% and annual nitrate loads by 10%. Forecasts of aboveground measures did not dramatically change with assimilation, a fact which highlights the limited potential of soil moisture as a constraint for a site with no water stress. Extending the scope of previous work, our results demonstrate the power of data assimilation to constrain important model estimates beyond the assimilated state variable, such as nitrate leaching. Replication of this study is necessary to further define the limitations and opportunities of the developed system.
KW - APSIM
KW - Agricultural forecasting
KW - Filter divergence
KW - Nitrate leaching
KW - Soil moisture
KW - State-parameter data assimilation
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UR - http://www.scopus.com/inward/citedby.url?scp=85123285783&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2022.153192
DO - 10.1016/j.scitotenv.2022.153192
M3 - Article
C2 - 35063525
AN - SCOPUS:85123285783
SN - 0048-9697
VL - 820
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 153192
ER -