TY - JOUR
T1 - The cycles agroecosystem model
T2 - Fundamentals, testing, and applications
AU - Kemanian, Armen R.
AU - Shi, Yuning
AU - White, Charles Macaulay
AU - Montes, Felipe
AU - Stöckle, Claudio O.
AU - Huggins, David R.
AU - Laura Cangiano, Maria
AU - Stefani-Faé, Giovani
AU - Nydegger Rozum, Rachel K.
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/12
Y1 - 2024/12
N2 - Models of the soil–plant-atmosphere continuum are repositories of knowledge and gears in analytical and decision support tools applied to agroecosystems. In this paper, we present the Cycles agroecosystem model theory along with test cases and applications. Cycles combines innovations for simulating soil hydrology and biogeochemistry, including carbon and nitrogen saturation theory, with a modular software architecture. These elements enable simulating monoculture or polyculture crop sequences and associated management practices, containerizing for applications that require high-performance computing, and data assimilation at runtime. A comparison of simulated and measured daily evapotranspiration (ET) obtained with the eddy covariance method for maize (Zea mays L.) and shrub willow (Salix spp.) shows that Cycles represents well meteorological and vegetation controls of ET (root mean square error or RMSE = 0.75 mm d-1). Cycles accurately simulated differences of 150 mm in growing season ET between these two plant communities. Comparisons of modeled versus measured soil water content under soybean (Glycine max [L.] Merr.) in southeastern Pennsylvania for six soil layers at 0.1-m increments showed accurate representation of water depletion and recharge (RMSE of 0.027–0.011 m3 m−3). Simulations of growth and nitrogen uptake of wheat (Triticum aestivum L.) in eastern Washington also highlight the model's skill simulating processes that affect water and nutrient fluxes simultaneously. To highlight Cycles’ suitability for incorporation in high performance computing applications, we present a coupling of Cycles with an autonomous crop sequence builder (Cycles-A) in the Chesapeake Bay watershed. This system automatically identified areas for double cropping and selected the optimum combination of annual crops across the watershed. The Cycles model innovations and agroecosystem framing continue advancing the premise of making models not only dynamic knowledge repositories but useful tools for research and landscape management.
AB - Models of the soil–plant-atmosphere continuum are repositories of knowledge and gears in analytical and decision support tools applied to agroecosystems. In this paper, we present the Cycles agroecosystem model theory along with test cases and applications. Cycles combines innovations for simulating soil hydrology and biogeochemistry, including carbon and nitrogen saturation theory, with a modular software architecture. These elements enable simulating monoculture or polyculture crop sequences and associated management practices, containerizing for applications that require high-performance computing, and data assimilation at runtime. A comparison of simulated and measured daily evapotranspiration (ET) obtained with the eddy covariance method for maize (Zea mays L.) and shrub willow (Salix spp.) shows that Cycles represents well meteorological and vegetation controls of ET (root mean square error or RMSE = 0.75 mm d-1). Cycles accurately simulated differences of 150 mm in growing season ET between these two plant communities. Comparisons of modeled versus measured soil water content under soybean (Glycine max [L.] Merr.) in southeastern Pennsylvania for six soil layers at 0.1-m increments showed accurate representation of water depletion and recharge (RMSE of 0.027–0.011 m3 m−3). Simulations of growth and nitrogen uptake of wheat (Triticum aestivum L.) in eastern Washington also highlight the model's skill simulating processes that affect water and nutrient fluxes simultaneously. To highlight Cycles’ suitability for incorporation in high performance computing applications, we present a coupling of Cycles with an autonomous crop sequence builder (Cycles-A) in the Chesapeake Bay watershed. This system automatically identified areas for double cropping and selected the optimum combination of annual crops across the watershed. The Cycles model innovations and agroecosystem framing continue advancing the premise of making models not only dynamic knowledge repositories but useful tools for research and landscape management.
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U2 - 10.1016/j.compag.2024.109510
DO - 10.1016/j.compag.2024.109510
M3 - Article
AN - SCOPUS:85205559254
SN - 0168-1699
VL - 227
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 109510
ER -