Greenhouse gas mitigation potential under different rice-crop rotation systems: from site experiment to model evaluation

Abstract

Crop rotation systems in fields could improve crop production and indirectly affect carbon and nitrogen dynamics due to multiple fertilizer applications. Therefore, it is important to evaluate the impact of these crop rotation systems on greenhouse gas (GHG) emissions. Field experiments were conducted with three different rotation systems, and the DeNitrification-DeComposition (DNDC) model was applied to monitor and estimate crop yields and GHG emissions during three rice-upland crop rotational periods (from June 2013 to May 2016). Low methane (CH 4 ) and nitrous oxide (N 2 O) emissions were observed in rice-Chinese milk vetch and single rice rotation systems. The simulated crop yields fit the observed data very well (d > 0.80, EF > 0.70) after the model was calibrated by adjusting the crop parameters. The model-simulated daily and annual CH 4 emissions agreed well with the field measurements (d > 0.7, EF = 0.41-0.98). The simulated N 2 O (d = 0.01-0.70, EF < 0 or close to 0) of accumulated emissions and daily fluxes (d = 0.07-0.14, EF < 0) showed low levels of accuracy with field observations. The GHG emissions in Shanghai rice paddy were different under various rotation systems, with the following order: single rice rotation system < rice-Chinese milk vetch rotation system < baseline rotation system of Shanghai paddy < 1/3 (rice-Chinese milk vetch + single rice + rice-winter wheat rice-winter) rotation system < rice-winter wheat rotation system under the same nitrogen loading. The DNDC was able to predict rice yields and GHG emissions under different crop rotation systems. The results provide positive reliable indications that crop rotation systems have the potential to reduce GHG emissions; e.g., the rice-Chinese milk vetch rotation system could mitigate the CH 4 and N 2 O emissions.

Rice Management Decisions Using Process-Based Models With Climate-Smart Indicators

“…The observed and simulated GHGs emissions data resulted in a good fit of Net-GWP despite the underestimation of N 2 O emissions; except in the case of the CSS treatment during the 2016/2017 season, in which the N 2 O represents the main contributor of overall GWP. Similar results were reported by Zhang et al (2019) argue that, despite discrepancies in the N 2 O simulation, owing to the strong agreement with methane fluxes and the low contribution of N 2 O the model can be used to estimate GWPs from tropical paddy fields. The poor performance of the model in simulating N 2 O may occur because the model assumes homogeneous microbial distribution and over/underestimates the soil moisture under different soil drainage conditions (Tonitto et al, 2010).…”

Modelling methane emissions and grain yields for a double-rice system in Southern China with DAYCENT and DNDC models

“… Cheng et al (2013) developed and evaluated the DAYCENT CH 4 module using total 97 rice paddy sites across China, with an overall r of 0.83 for model predictions vs measurements. In addition, the DNDC model has been corroborated by many CH 4 emission datasets from Chinese rice fields, and the simulated values are generally in good agreement with the observed CH 4 field emissions ( Zhang et al, 2002 , Li et al, 2006 , Zhang et al, 2019 , Zhao et al, 2020 , Wang et al, 2021a , Wang et al, 2021b ).…”

“…Similarly, the DNDC model was also calibrated on crop yield/annual CH 4 emissions for the site using the measured data from the control with treatment S. Model calibration for crop yields and CH 4 emissions was done by optimizing a combination of different crop growth parameters, including maximum biomass production, biomass fraction, biomass C/N ratio, thermal degree days ( Table 3 ), as suggested by Zhang et al, 2019 , Abdalla et al, 2020 . Crop parameter input default values were tested until the DNDC model matched the measured grain yield/annual CH 4 emission values from the control treatment S. The calibrated model was then used to run those for another two treatments WS and W from November 2008 to November 2014.…”

“…Simulation models allow complex interactions and real-world problems to be examined in a cost- and time-effective way ( Giltrap et al, 2010 , Cheng et al, 2013 ). DAYCENT and DeNitrification-DeComposition (DNDC) are two popular ecological process-based models to simulate methane (CH 4 ) emissions from rice paddy fields in China ( Li et al, 2006 , Cheng et al, 2014 , Zhang et al, 2019 , Wang et al, 2021a , Wang et al, 2021b ). Cheng et al (2013) developed and evaluated the DAYCENT CH 4 module using total 97 rice paddy sites across China, with an overall r of 0.83 for model predictions vs measurements.…”

Estimates of methane and nitrous oxide emission from a rice field in Central Java, Indonesia, based on the DeNitrification DeComposition model

“…The DeNitrification DeComposition (DNDC) model is a high-efficiency tool for simulating rice yields and for capturing CH4 and N2O emissions from different rice production systems (Zhang et al, 2021). In addition, many studies have modified and optimized the DNDC model (Han et al, 2014;Zhang et al, 2019), which can simulate soil temperature and moisture accurately. Temperature and humidity are very important for nitrification and denitrification because they determine the activity of microorganisms.…”