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Carbon fluxes from agroecosystems contribute to the variability of the carbon cycle and atmospheric [CO 2 ]. This study is a follow-up to Gahlot et al. (2020), which used the Integrated Science Assessment Model (ISAM) to examine spring wheat production and its drivers. In this study, we look at the carbon fluxes and their drivers. ISAM was calibrated and validated against the crop phenology at the IARI wheat experimental site in Gahlot et al. (2020). We extended the validation of the model on a regional scale by comparing modeled leaf area index (LAI) and yield against site-scale observations and regional datasets. Later, ISAM-simulated carbon fluxes were validated against an experimental spring wheat site at IARI for the growing season of 2013–2014. Additionally, we compared with the published carbon flux data and found that ISAM captures the seasonality well. Following that, regional-scale runs were performed. The results revealed that fluxes vary significantly across regions, primarily owing to differences in planting dates. During the study period, all fluxes showed statistically significant increasing trends (p<0.1). Gross primary production (GPP), net primary production (NPP), autotrophic respiration (Ra), and heterotrophic respiration (Rh) increased at 1.272, 0.945, 0.579, 0.328, and 0.366 TgC yr -2 , respectively. Numerical experiments were conducted to investigate how natural forcings such as changing temperature and [CO 2 ] levels as well as agricultural management practices such as nitrogen fertilization and water availability could contribute to the rising trends. The experiments revealed that increasing [CO 2 ], nitrogen fertilization, and irrigation water contributed to increased carbon fluxes, with nitrogen fertilization having the most significant effect. [ABSTRACT FROM AUTHOR]

Devising effective national-level climate action plans needs a more detailed understanding of the regional distribution of sources and sinks of greenhouse gases. Due to insufficient observations and modelling capabilities, India's current carbon source-sink estimates are uncertain. This study uses a high-resolution transport model to examine the potential of CO2 observations over India for inverse estimation of regional carbon fluxes. We make use of four different sites in India that vary in measurement technique, frequency and spatial representation. These observations exhibit substantial seasonal (7.5 to 9.2 ppm) and intra-seasonal (2 to 12 ppm) variability. Our modelling approach, a high-resolution Weather Research and Forecasting Model combined with Stochastic Time Inverted Lagrangian Transport (WRF-STILT) model, performs better in simulating seasonal (R2 = 0.50 to 0.96) and diurnal (R2 = 0.96) variability of observed CO2 than the current generation global models analysed in the study. Representation of local flux variability like biomass burning in the model needs further refinement, depending on the site location. During the agricultural season, crop biospheric uptake in the Indo-Gangetic Plain region significantly modulates the CO2 variability in the northern Indian stations. Depending on the region and time of the year, the anthropogenic and biospheric emission components contribute differently to CO2 variability. The choice of emission inventory in the modelling framework alone leads to significant biases in simulations (5 to 10 ppm), endorsing the need for accounting emission fluxes, especially for non-background sites. By implementing a high-resolution model, our results emphasise that observations from Indian sites can be useful in deducing carbon flux information at regional (Nainital) and sub-urban to urban (Mohali, Shadnagar, Nagpur) scales. On accounting for observed variability, the global carbon data assimilation system can thus benefit from the measurements from the Indian subcontinent. [ABSTRACT FROM AUTHOR]