Assessment and application of EPIC in simulating upland rice productivity, soil water, and nitrogen dynamics under different nitrogen applications and planting windows

Authors: HUSSAIN, TAJAMUL - Prince Of Songkla University; Gollany, Hero; BEN, ZHAO - Henan Agricultural University; TAHIR, MUHAMMAD - University Of Minnesota; TAHIR ATA-UL-KARIM, SYED - University Of Tokyo; LIU, KE - University Of Tokyo; MAQBOOL, SALIHAL - University Of Minnesota; HUSSAIN, NURDA - Prince Of Songkla University; DUANGPAN, SAOWAPA - Prince Of Songkla University

Submitted to: Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/28/2023
Publication Date: 9/13/2023
Citation: Hussain, T., Gollany, H.T., Ben, Z., Tahir, M., Tahir Ata-Ul-Karim, S., Liu, K., Maqbool, S., Hussain, N., Duangpan, S. 2023. Assessment and application of EPIC in simulating upland rice productivity, soil water, and nitrogen dynamics under different nitrogen applications and planting windows. Journal of Environmental Management. 13(9).2379. https://doi.org/10.3390/agronomy13092379.
DOI: https://doi.org/10.3390/agronomy13092379

Interpretive Summary: A suitable nitrogen (N) application rate (NAR) and ideal planting period could improve upland rice productivity, enhance soil moisture utilization, and reduce N losses. The objective of this study was to assess the performance of the EPIC model to simulate upland rice productivity, soil water and N dynamics under different NAR and planting windows. The nitrogen treatments were 26.8, 53.6, and 80.4 lb/ac with a control (no N applied -N0). Planting was performed as early, moderately delayed, and delayed between September and December of each growing season. The results indicated that the N application rate and planting windows impacted upland rice responses and the EPIC model was able to simulate grain yield, aboveground biomass, and harvest index for all planting windows with a normalized good to excellent root mean square error of 7.4–9.4%, 9.9–12.2% and 2.3–12.4% for the grain yield, aboveground biomass, and harvest index, respectively. For grain and total plant N uptake, root mean square error ranged from 10.3–22.8% and 6.9–28.1%, respectively. Evapotranspiration was slightly underestimated for all N application rates at all planting windows in both seasons with root mean square error ranging from 2.0–3.1%. A comparison of N and water balance components indicated that planting windows was the major factor impacting N and water losses as compared to the N application rate. There was a good agreement between simulated and observed soil water contents, and the model was able to estimate fluctuations in soil water contents for two soil layers in all treatments. An adjustment in the planting window would be necessary for improved upland rice productivity, enhanced N, and soil water utilization to reduce N and soil water losses. Our results indicate that a well-calibrated EPIC model has the potential to identify suitable N and seasonal planting management options.

Technical Abstract: A suitable nitrogen (N) application rate (NAR) and ideal planting period could improve upland rice productivity, enhance soil moisture utilization, and reduce N losses. The objective of this study was to assess the performance of the EPIC model to simulate upland rice productivity, soil water and N dynamics under different NAR and planting windows (PW). The nitrogen treatments were 30 (N30), 60 (N60), and 90 (N90) kg N ha-1 with a control (no N applied -N0). Planting was performed as early (PW1), moderately delayed (PW2) and delayed (PW3) between September and December of each growing season. The NAR and PW impacted upland rice responses and the EPIC model predicted grain yield, aboveground biomass, and harvest index for all NARs in all PWs with a normalized good-excellent root mean square error (RMSEn) of 7.4–9.4%, 9.9–12.2% and 2.3–12.4%, and d–index of 0.90–0.98, 0.87–0.94 and 0.89–0.91 for grain yield, aboveground biomass, and harvest index, respectively. For grain and total plant N uptake, RMSEn ranged fair to excellent with values ranging from 10.3–22.8% and 6.9–28.1% and a d–index of 0.87–0.97 and 0.73–0.99, respectively. Evapotranspiration was slightly under-estimated for all NARs at all PWs in both seasons with excellent RMSEn ranging from 2.0–3.1% and d–index ranging from 0.65–0.97. A comparison of N and water balance components indicated that PW was the major factor impacting N and water losses as compared to NAR. There was a good agreement between simulated and observed soil water contents, and the model was able to estimate fluctuations in soil water contents. An adjustment in the planting window would be necessary for improved upland rice productivity, enhanced N, and soil water utilization to reduce N and soil water losses. Our results indicated that a well calibrated EPIC model has the potential to identify suitable N and seasonal planting management options.