Different responses of agroecosystem greenhouse gas emissions to tillage practices in a Chinese wheat–maize cropping system

Abstract

To mitigate greenhouse gas (GHG) emissions of intensified agriculture, conservation practices are gradually being implemented in Chinese wheat–maize cropping systems. However, the effects of different tillage practices on agricultural field GHG emissions and subsequent global warming potential (GWP) are poorly documented. In this study, a three-year field experiment was conducted from 2019 to 2021 to assess the effects of tillage on the emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and eventually GWP. Compared to conventional tillage (CT), no-tillage (NT) significantly decreased CO2, CH4, and N2O emissions by 35.43%, 67.33%, 339.07%, respectively, which resulted in a decrease of 37.25% in GWP during three annual cycles. Based on the results of this study, soil could potentially act as a net source of CO2 and CH4 under both CT and NT, and a net sink of N2O under NT. Annually, non-growing season contributed 16.9%, 15.6%, and 13.8% soil CO2, CH4, and N2O fluxes, and 16.6% GWP under CT and 17.3%, 16.4%, 21.6%, and 17.3% under NT, respectively. Compared to CT, NT improved the aboveground biomass and grain yields of wheat by 21.3% and 13.3% from averaged results, respectively; no significant differences were found for maize yields. Although principal component analysis showed that soil temperature had higher correlations with CO2 emissions and GWP as compared to soil moisture, soil moisture affected GHG emissions more than soil temperature as demonstrated by the structural equation model. The modeling analysis found that NT increased soil moisture, pH, and bulk density, thus increasing soil organic carbon and decreasing total nitrogen content, eventually inhibiting GHG emissions. This research demonstrated that NT had the potential to mitigate GHG emissions, yet stability needed further investigation on long-term scales.∙ Graphical Abstract

A Cross-Sectional Analysis of the Relationship between Digital Technology Use and Agricultural Productivity in EU Countries

“…Previous studies [3,4,[80][81][82] have demonstrated that SEM is a powerful and flexible method for analyzing the complex relationships among multiple and diverse variables affecting the agricultural sector. SEM allows researchers to model intricate interactions among observed and unobserved variables, making it well suited for testing and validating complex theoretical models, as emphasized by Garson [79] and Hair et al [78].…”

A Longitudinal Analysis of the Impact of Digital Technologies on Sustainable Food Production and Consumption in the European Union

“…SEM enables the simultaneous evaluation of multiple variables and their interdependencies, making it suitable for studying the multidimensional dynamics of digital technology's impact on sustainable production and consumption, as well as GHGs in agriculture [103]. Previous research in the field of agriculture has successfully employed SEM in investigating influences among heterogeneous variables [104][105][106].…”

“…SEM provides several advantages in this context. Firstly, it allows for testing hypotheses derived from the theoretical model, enabling researchers to assess the direct and indirect effects of digital technologies on various aspects of sustainable production and consumption [105]. Secondly, SEM enables the integration of multiple data sources, such as DESI reports, Sustainable Development Reports, and the Eurostat database [107].…”

Life cycle assessment of Nicotiana tabacum L.: sustainability of seedling alternatives

“…Soil tillage is one of the operations with the highest energy consumption, emitting a large amount of GHG (Li et al, 2023). Currently, traditional tillage systems have high fuel consumption, while optimized tillage systems can save signi cantly in terms of energy without compromising crop productivity (Jankowski et al, 2023).…”