Background: Soil respiration (S R) is a critical process for understanding the impact of climatic conditions and land degradation on the carbon cycle in terrestrial ecosystems. We measured the S R and soil environmental factors over 1 year in four land uses with varying levels of disturbance and different vegetation types viz., mixed forest cover (MFC), Prosopis juliflora (Sw.) forest cover (PFC), agricultural field (AF), and vegetable field (VF), in a semi-arid area of Delhi, India. Our primary aim was to assess the effects of soil moisture (S M), soil temperature (S T), and soil microbial activity (S MA) on the S R. Methods: The S R was measured monthly using an LI-6400 with an infrared gas analyser and a soil chamber. The S M was measured using the gravimetric method. The S T (10 cm) was measured with a probe attached to the LI-6400. The S MA was determined by fluorescein diacetate hydrolysis. Results: The S R showed seasonal variations, with the mean annual S R ranging from 3.22 to 5.78 μmol m −2 s −1 and higher S R rates of~15-55% in the cultivated fields (AF, VF) than in the forest sites (MFC, PFC). The VF had significantly higher S R (P < 0.05) than the other land uses (AF, PFC, MFC), which did not vary significantly from one another in S R (P < 0.05). The repeated measures ANOVA evaluated the significant differences (P < 0.05) in the S R for high precipitation months (July, August, September, February). The S M as a single factor showed a strong significant relationship in all the land uses (R 2 = 0.67-0.91, P < 0.001). The effect of the S T on the S R was found to be weak and non-significant in the PFC, MFC, and AF (R 2 = 0.14-0.31; P > 0.05). Contrasting results were observed in the VF, which showed high S R during summer (May; 11.21 μmol m −2 s −1) and a significant exponential relationship with the S T (R 2 = 0.52; P < 0.05). The S R was positively related to the S MA (R 2 = 0.44-0.5; P < 0.001). The interactive equations based on the independent variables S M , S T , and S MA explained 91-95% of the seasonal variation in S R with better model performance in the cultivated land use sites (AF, VF). Conclusion: S M was the key determining factor of the S R in semi-arid ecosystems and explained~90% of the variation. Precipitation increased S R by optimizing the S M and microbial activity. The S MA , along with the other soil factors S M and S T , improved the correlation with S R. Furthermore, the degraded land uses will be more susceptible to temporal variations in S R under changing climatic scenarios, which may influence the carbon balance of these ecosystems.