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Title: Simulating county-level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization

Representing agricultural systems explicitly in Earth system models is important for understanding the water-energy-food nexus under climate change. In this study, we applied Version 4.5 of the Community Land Model (CLM) at a 0.125 degree resolution to provide the first county-scale validation of the model in simulating crop yields over the Conterminous United States (CONUS). We focused on corn and soybean that are both important grain crops and biofuel feedstocks (corn for bioethanol; soybean for biodiesel). We find that the default model substantially under- or over-estimate yields of corn and soybean as compared to the US Department of Agriculture (USDA) census data, with corresponding county-level root-mean square error (RMSE) of 45.3 Bu/acre and 12.9 Bu/acre, or 42% and 38% of the US mean yields for these crops, respectively. Based on the numerical experiments, the lack of proper representation of agricultural management practices, such as irrigation and fertilization, was identified as a major cause for the model's poor performance. After implementing an irrigation management scheme calibrated against county-level US Geological Survey (USGS) census data, the county-level RMSE for corn yields reduced to 42.6 Bu/acre. We then incorporated an optimized fertilizer scheme in rate and timing, which is achieved by the constrainingmore » annual total fertilizer amount against the USDA data, considering the dynamics between fertilizer demand and supply and adopting a calibrated fertilizer scheduling map. The proposed approach is shown to be effective in increasing the fertilizer use efficiency for corn yields, with county-level RMSE reduced to 23.8 Bu/acre (or 22% of the US mean yield). In regions with similar annual fertilizer applied as in the default, the improvements in corn yield simulations are mainly attributed to application of longer fertilization periods and consideration of the dynamics between fertilizer demand and supply. For soybean which is capable of fixing nitrogen to meet nitrogen demand, the reduced positive bias to 6.9 Bu/acre (or 21% of the country mean) was mainly attributed to consideration of the dynamic interactions between fertilizer demand and supply. Although large bias remains in terms of the spatial pattern (i.e. high county-level RMSE), mainly due to limited performance over the Western US, our results show that optimizing irrigation and fertilization can lead to promising improvement in crop and soybean yield simulations in terms of the mean and variability especially over the Mid-west corn belt, and subsequent evapotranspiration (ET) estimates. Finally, this study demonstrates the CLM4.5 capability for predicting crop yields and their interactions with climate, and highlights the value of continued model improvements and development to understand biogeophysical and biogeochemical impacts of land use and land cover change using an Earth system modeling framework.« less
Authors:
 [1] ;  [1] ; ORCiD logo [2] ;  [1] ;  [1] ;  [1] ; ORCiD logo [2]
  1. Pacific Northwest National Lab. (PNNL), College Park, MD (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Report Number(s):
PNNL-SA-116300
Journal ID: ISSN 1942-2466; KP1703030
Grant/Contract Number:
AC05-76RL01830
Type:
Accepted Manuscript
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
Journal Volume: 8; Journal Issue: 4; Journal ID: ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES
OSTI Identifier:
1346294

Leng, Guoyong, Zhang, Xuesong, Huang, Maoyi, Yang, Qichun, Rafique, Rashid, Asrar, Ghassem R., and Ruby Leung, L.. Simulating county-level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization. United States: N. p., Web. doi:10.1002/2016MS000645.
Leng, Guoyong, Zhang, Xuesong, Huang, Maoyi, Yang, Qichun, Rafique, Rashid, Asrar, Ghassem R., & Ruby Leung, L.. Simulating county-level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization. United States. doi:10.1002/2016MS000645.
Leng, Guoyong, Zhang, Xuesong, Huang, Maoyi, Yang, Qichun, Rafique, Rashid, Asrar, Ghassem R., and Ruby Leung, L.. 2016. "Simulating county-level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization". United States. doi:10.1002/2016MS000645. https://www.osti.gov/servlets/purl/1346294.
@article{osti_1346294,
title = {Simulating county-level crop yields in the Conterminous United States using the Community Land Model: The effects of optimizing irrigation and fertilization},
author = {Leng, Guoyong and Zhang, Xuesong and Huang, Maoyi and Yang, Qichun and Rafique, Rashid and Asrar, Ghassem R. and Ruby Leung, L.},
abstractNote = {Representing agricultural systems explicitly in Earth system models is important for understanding the water-energy-food nexus under climate change. In this study, we applied Version 4.5 of the Community Land Model (CLM) at a 0.125 degree resolution to provide the first county-scale validation of the model in simulating crop yields over the Conterminous United States (CONUS). We focused on corn and soybean that are both important grain crops and biofuel feedstocks (corn for bioethanol; soybean for biodiesel). We find that the default model substantially under- or over-estimate yields of corn and soybean as compared to the US Department of Agriculture (USDA) census data, with corresponding county-level root-mean square error (RMSE) of 45.3 Bu/acre and 12.9 Bu/acre, or 42% and 38% of the US mean yields for these crops, respectively. Based on the numerical experiments, the lack of proper representation of agricultural management practices, such as irrigation and fertilization, was identified as a major cause for the model's poor performance. After implementing an irrigation management scheme calibrated against county-level US Geological Survey (USGS) census data, the county-level RMSE for corn yields reduced to 42.6 Bu/acre. We then incorporated an optimized fertilizer scheme in rate and timing, which is achieved by the constraining annual total fertilizer amount against the USDA data, considering the dynamics between fertilizer demand and supply and adopting a calibrated fertilizer scheduling map. The proposed approach is shown to be effective in increasing the fertilizer use efficiency for corn yields, with county-level RMSE reduced to 23.8 Bu/acre (or 22% of the US mean yield). In regions with similar annual fertilizer applied as in the default, the improvements in corn yield simulations are mainly attributed to application of longer fertilization periods and consideration of the dynamics between fertilizer demand and supply. For soybean which is capable of fixing nitrogen to meet nitrogen demand, the reduced positive bias to 6.9 Bu/acre (or 21% of the country mean) was mainly attributed to consideration of the dynamic interactions between fertilizer demand and supply. Although large bias remains in terms of the spatial pattern (i.e. high county-level RMSE), mainly due to limited performance over the Western US, our results show that optimizing irrigation and fertilization can lead to promising improvement in crop and soybean yield simulations in terms of the mean and variability especially over the Mid-west corn belt, and subsequent evapotranspiration (ET) estimates. Finally, this study demonstrates the CLM4.5 capability for predicting crop yields and their interactions with climate, and highlights the value of continued model improvements and development to understand biogeophysical and biogeochemical impacts of land use and land cover change using an Earth system modeling framework.},
doi = {10.1002/2016MS000645},
journal = {Journal of Advances in Modeling Earth Systems},
number = 4,
volume = 8,
place = {United States},
year = {2016},
month = {11}
}