Abstract:Under global warming and elevated nitrogen deposition, it becomes an urgent problem to find out how farmland soil respiration responds to climate warming and increasing nitrogen deposition in the North China Plain, one of the main grain-producing areas in China. In this study, the soil respiration rate and temperature sensitivity were measured using a static chamber gas chromatography method from 2018 to 2020. The soil respiration rate and temperature sensitivity were determined by field heating and nitrogen application for 11 years. Three treatments: infrared warming (W) (with an annual average increase of 1.5 °C according to our previous results), nitrogen fertilization (N) (240 kg(N)∙hm ⁻²∙a ⁻¹urea), and combined warming and nitrogen fertilization (WN) were used in this study. An untreated control treatment (CK) was also included. The results showed that the W and WN treatments increased soil temperature at 5 cm depth by approximately 2 °C on average and decreased soil water content by 2.4% from 2018 to 2020. The average soil respiration rate (329.06 mg∙m ⁻²∙h ⁻¹) in the growing season from March to June was significantly higher than that in the dormancy season from November to March (25.21 mg∙m ⁻²∙h ⁻¹) ( P< 0.05). From 2018 to 2020, the W and WN treatments increased the soil respiration rate by 16.8% and 19.3%, compared with CK, respectively ( P< 0.05). The N treatment had no significant effect on the soil respiration rate. During the same period, the temperature sensitivity ( Q ₁₀) of soil respiration in the W and WN treatments was lower than that in the N and CK treatments, that was in the order of WN (1.65) < W (1.70) < N (1.78) < CK (1.80). The Q ₁₀of soil respiration showed obvious seasonal variations, with an average high of 2.93 in the winter dormancy season and an average low of 1.81 in the summer growing season. This study showed that the temperature sensitivity of the soil respiration was decreased as temperature increased, and that Q ₁₀showed significant seasonal differences. This information will help improve the accuracy of future carbon estimation models.