Nutrient supply enhanced wheat residue-carbon mineralization, microbial growth, and microbial carbon-use efficiency when residues were supplied at high rate in contrasting soils

Abstract

Crop residue is a source of energy and nutrients for microbial growth, and can be converted into soil organic matter (SOM). However, there are still knowledge gaps on how the interaction of crop residue and supplementary nutrients (nitrogen, phosphorus and sulfur) influence the fate of residue in microbial biomass carbon (MBC) and microbial use efficiency. Here, we quantified the carbon (C) mineralization of wheat residue (δ13C 494‰) and microbial C use efficiency at ecosystem scale (CUEE) with two levels of the residue (6.7 and 20.0 g kg−1 soil) and three levels of nutrients in Luvisol and Vertisol under a controlled laboratory condition (22 °C). Between 48 and 61% of residue-C was mineralized over 245 days. The residue CUEE was similar across the two levels of the residue on day 3 (i.e., 0.29–0.40 in the Luvisol and 0.41–0.50 in the Vertisol) and then decreased over time, likely because of greater increase in cumulative total respiration and microbial turnover, relative to microbial growth. The residue CUEE was 20–55% lower in the high-residue, compared with (cf.) low-residue, input after day 10, which may have resulted from the greater loss of C via overflow respiration (microbial respiration without the production of energy) and turnover of high MBC. Narrowing of the C-nutrient imbalance increased residue-C mineralization, residue-derived MBC, and residue CUEE only under the high-residue input scenario where nutrients were possibly a limiting factor for microbial growth. Consequently, the results indicate a higher efficiency of SOM formation from returning of residue to soils under (i) low-residue (cf. high-residue) with or without nutrients, and (ii) high-residue with nutrient inputs (cf. no nutrient). The findings of the integrated residue–nutrient management on dynamics of residue-derived MBC and residue CUEE can be used to improve predictive performance of the models on specific soil functions such as SOM storage in agro-ecosystems.