Composted sewage sludge (CS) is considered a rich source of soil nutrients and significantly affects the physical, chemical, and biological characteristics of soil, but its effect on specific enzyme activity in soil is disregarded. The present experiment examined the absolute and specific enzyme activity of the enzymes involved in carbon, nitrogen, and phosphorus cycles, the diversity of soil microbial functions, and soil community composition in a Fluventic Ustochrept under a maize—wheat rotation system in North China during 2012–2015. Application of CS led to increase in MBC and in its ratio to both total organic carbon (TOC) and microbial biomass nitrogen (MBN). Absolute enzyme activity, except that of phosphatase, increased in CS-treated soils, whereas specific activity of all the enzymes declined, especially at the highest dose of CS (45 t ha⁻¹). The diversity of soil microbial community also increased in CS-treated soils, whereas its functional diversity declined at higher doses of CS owing to the lowered specific enzyme activity. These changes indicate that CS application induced the domination of microorganisms that are not metabolically active and those that use resources more efficiently, namely fungi. Redundancy analysis showed that fundamental alterations in soil enzyme activity depend on soil pH. Soil specific enzyme activity is affected more than absolute enzyme activity by changes in soil properties, especially soil microbial activity and composition of soil microflora (as judged by the following ratios: MBC/TOC, MBC/MBN, and TOC/LOC, that is labile organic carbon) through the Pearson Correlation Coefficient. Specific enzyme activity is thus a more accurate parameter than absolute enzyme activity for monitoring the effect of adding CS on the activities and structure of soil microbial community.

Reducing the use of nitrogen fertilizers and returning straw to field are being promoted in northeast China (NEC). In this paper, the agricultural production system model (APSIM) was applied to assess the long-term variations of crop yield and soil GHG emissions in a maize mono-cropping system of NEC, and the simulation results were combined with lifecycle assessment to estimate annual GHG emissions (GHG_L) and GHG emission intensity (GHG_I, GHG emissions per unit yield) of different agricultural practices. Under current farmers’ practice, emissions due to machinery input (including production, transportation, repair, and maintenance) and soil organic carbon (SOC) decline accounted for 15% of GHG_L, while emissions from nitrogen fertilizer input (production and transportation) and direct N₂O emissions from soil accounted for the majority (~60% of GHG_L). Current farmers’ practice in terms of N application and residue management are nearly optimal for crop production but not for climate change mitigation. Reducing N input by 13% and increasing straw retention by 20% can maintain crop yield and SOC, and also reduce GHG_L and GHG_I by 13% and 11%, respectively. However, it is not feasible to incorporate the straw used as household fuel into soil, which could incur substantial fossil CO₂ emissions of 3.98 Mg CO₂-eq ha⁻¹ resulting from the substitution of coal for straw. APSIM was successful in simulating crop yield, N₂O emissions, and SOC change in NEC, and our results highlight opportunities to further optimize management strategies (especially for the nitrogen and straw management) to reduce GHG emissions while maintaining crop yield.

Greenhouse vegetable production in China not only increases farmers’ income, but also increases the risk of nitrogen losses due to excessive water and fertilizer input. Nitrogen losses, including the potent greenhouse gas nitrous oxide (N₂O), are driven by water content, soil temperature and pH; regulated by available organic carbon and inorganic nitrogen (N); and affected by management. Therefore, a process-based model was applied to explain the complex interaction of the factors affecting N losses in the form of N₂O, NH₃ and NO₃⁻ from a greenhouse vegetable production system in a northeast suburb of Beijing, China. We designed four treatments: two equal N input treatments with one flooding (FP) and the other drip irrigation (FPD) and two equal water input treatments (drip irrigation) with one 100% chemical N input (FPD) and the other 50% N input (OPTD). The last one was CK treatment (flooding without chemical N). We calibrated the WHCNS-veg model using year-round measurements of soil temperature, N₂O emission, NH₃volatilization, NO₃⁻ distribution and yields for greenhouse cucumber–tomato cultivation under farmers’ practice (flooding + 100% chemical N, FP). Then, we validated the model using the data sets under drip irrigation (70% of flooding amount + 100% chemical N, FPD), reduced chemical N by 50% (drip + 50% chemical N, OPTD) and CK treatment. The WHCNS-veg model was able to capture the above processes under different treatments. Annual N₂O emissions were 5.47 and 3.76kg N ha⁻¹ for the cucumber and tomato seasons under FP, respectively. Compared to FP, drip irrigation (FPD) decreased N₂O emissions by 19.0% and 45.5% in the two seasons, respectively. Compared to FPD, applying a lower rate of N (OPTD) further reduced N₂O emissions by 13.7% and 40.5%, respectively. According to the model simulation, N₂O emission was mainly controlled by nitrification/denitrification in the cucumber/tomato seasons, respectively. Compared to FP, drip irrigation (FPD) increased NH₃ volatilization by 54.2% in the cucumber season, while in the tomato season, there were no significant differences inNH₃ volatilization under the three fertilizer treatments. The nitrate leaching levels were 48.5 and 81.0 kg N ha⁻¹ for the two seasons under FP treatment. Drip irrigation (FPD) decreased NO₃⁻ leaching by 20.6% in the cucumber season. Drip irrigation (FPD) and/or reducing chemical N (OPTD) did not compromise vegetable yields. In all, WHCNS-veg performed well in simulating N₂O, NH₃ and NO₃⁻ dynamics from the greenhouse vegetable field, which means that the model can be used to manage water and nitrogen precisely in greenhouse vegetable production systems by scenario analysis, and drip irrigation and/or lower N input can be applied in this area to secure yield and reduce N losses.