Reduced fertiliser inputs: Managing antibiotic resistance genes along the soil-root-crop continuum

How can manure nutrients be returned to agricultural soils while reducing the accompanying pressure from antibiotic resistance genes? Our newly published article in Ecotoxicology and Environmental Safety examines this question through a field experiment comparing fertiliser sources and application rates in a Chinese cabbage cropping system (Wang et al., 2026).

The study brings together two concerns central to circular nutrient management: maintaining soil nutrient resources and understanding the environmental consequences of repeated manure application. I am pleased to share this collaborative publication with colleagues Han Wang, Shizhou Shen, Muheng Liu, Shenwei Cheng, Erik Sindhøj, Junfeng Liang, Yuanfang Zeng, Keqiang Zhang and Suli Zhi.

Continue reading ”Reduced Fertiliser Inputs and Antibiotic Resistance” →

How can livestock manure be managed so that its nutrients remain useful while the risks associated with antibiotic resistance are reduced? Our newly published article in the Journal of Environmental Chemical Engineering explores this question through the storage and fermentation of pig manure slurry (Cao et al., 2026).

The study examines the combined use of biochar and ferrous sulfate, bringing together two concerns that are central to circular nutrient management: the fate of antibiotic resistance genes and the transformation of nitrogen. I am pleased to share this publication with colleagues Yuang Cao, Zhuowu Li, Xiaoyu Xu, Jiahua Liu, Keqiang Zhang, Lianzhu Du and Suli Zhi.

Storage as an opportunity for intervention

Manure slurry contains nitrogen that can support agricultural production. It can also carry antibiotic resistance genes (ARGs), the genetic information that can enable microorganisms to resist antibiotics. The period before land application is therefore an important part of manure management. During storage and fermentation, changes in microbial communities and slurry chemistry can influence both nutrient availability and the persistence of resistance genes.

Our study focused on this stage using slurry collected from a pig farm in Tianjin, China. A 60-day laboratory experiment compared 13 treatment groups, with three replicate reactors per treatment, at approximately 30 °C. The treatments included biochar, ferrous sulfate, ferrous sulfate-modified biochar, physical mixtures of biochar and ferrous sulfate, and sulfuric acid, alongside an untreated control. Chemical measurements and metagenomic analysis were used to examine nitrogen forms, microbial communities and resistance-gene profiles.

Continue reading ”Biochar and ferrous sulfate: Managing antibiotic resistance genes and nitrogen in pig manure slurry” →

Development of a Rapid Ammonia Concentration Prediction Model for Pig Manure Reactor Composting Based on Infrared Spectroscopy

This study was conducted through an international collaboration involving Northeast Agricultural University and the Agro-Environmental Protection Institute of China’s Ministry of Agriculture and Rural Affairs, together with NIBIO the Norwegian Institute of Bioeconomy Research, and the University of Duisburg-Essen in Germany.

Abstract

To address the challenges of delayed sample collection, the loss of valid samples, and unstable concentration predictions during the monitoring of ammonia emissions from pig-manure reactor composting, this study developed an in situ online monitoring system integrating photoacoustic spectroscopy and infrared spectroscopy. A multi-indicator iterative outlier-removal method based on logarithmic binning, referred to as Method A, was developed and compared with a conventional single-indicator method using a global threshold, referred to as Method B. Partial least squares regression, random forest, and extremely randomized trees were used to construct quantitative ammonia-concentration prediction models for different concentration bins. Model performance was then systematically assessed using multiple evaluation indicators.

Compared with Method B, Method A increased the sample-retention rate by 22.4% and reduced the loss of dynamic range by 96.2%. During model development, the extremely randomized trees model achieved prediction coefficients of determination ranging from 0.894 to 0.973 across the concentration bins, with residual predictive deviation values of at least 3.0 and mean absolute error as a percentage of full scale ranging from ±2.19% to ±2.68%. Its performance was superior to that of the random forest and partial least squares models.

In subsequent validation, the extremely randomized trees models for the three concentration bins achieved coefficients of determination of 0.908, 0.978, and 0.989, respectively. The corresponding residual predictive deviation values were 3.217, 6.184, and 6.157, while the mean absolute errors as percentages of full scale were ±2.98%, ±3.20%, and ±3.59%, respectively. These results meet the requirements for high-precision quantitative analysis under complex operating conditions.

The findings demonstrate that combining logarithmic binning-based multi-indicator iterative outlier removal with extremely randomized trees enables rapid quantitative prediction of ammonia concentrations. This approach provides technical support for the accurate monitoring of ammonia-emission levels during reactor composting.

Keywords

Keywords: Pig manure; reactor composting; ammonia; quantitative prediction; infrared spectroscopy; outlier removal; prediction model.

Reference

Jia, Y., Yang, Z., Cordeiro, C. M., Sindhöj, E., Siesler, H. W., Qin, W., Zhao, R., & Zhang, K. (2026). 基于红外光谱的猪粪反应器堆肥氨排放浓度速测模型构建 / Development of a rapid ammonia concentration prediction model for pig manure reactor composting based on infrared spectroscopy. 农业环境科学学报 / Journal of Agro-Environment Science. Advance online publication. PDF download.