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Crop residue return sustains global soil ecological stoichiometry balance.

Ji LiuTianyi QiuJosep PenuelasJordi SardansWenfeng TanXiaomeng WeiYongxing CuiQingliang CuiChuanfa WuLanfa LiuBaitao ZhouHaoran HeLinchuan Fang
Published in: Global change biology (2023)
Although soil ecological stoichiometry is constrained in natural ecosystems, its responses to anthropogenic perturbations are largely unknown. Inputs of inorganic fertilizer and crop residue are key cropland anthropogenic managements, with potential to alter their soil ecological stoichiometry. We conducted a global synthesis of 682 data pairs to quantify the responses of soil carbon (C), nitrogen (N), and phosphorus (P) and grain yields to combined inputs of crop residue plus inorganic fertilizer compared with only inorganic fertilizer application. Crop residue inputs enhance soil C (10.5%-12%), N (7.63%-9.2%), and P (2.62%-5.13%) contents, with an increase in C:N (2.51%-3.42%) and C:P (7.27%-8.00%) ratios, and grain yields (6.12%-8.64%), indicating that crop residue alleviated soil C limitation caused by inorganic fertilizer inputs alone and was able to sustain balanced stoichiometry. Moreover, the increase in soil C and C:N(P) ratio reached saturation in ~13-16 years after crop residue return, while grain yield increase trend discontinued. Furthermore, we identified that the increased C, N, and P contents and C:N(P) ratios were regulated by the initial pH and C content, and the increase in grain yield was not only related to soil properties, but also negatively related to the amount of inorganic N fertilizer input to a greater extent. Given that crop residual improvement varies with soil properties and N input levels, we propose a predictive model to preliminary evaluate the potential for crop residual improvement. Particularly, we suggest that part of the global budget should be used to subsidize crop residue input management strategies, achieving to a win-win situation for agricultural production, ecological protection, and climate change mitigation.
Keyphrases
  • climate change
  • human health
  • plant growth
  • sewage sludge
  • risk assessment
  • machine learning
  • artificial intelligence
  • drug induced