Yang Guang, Pan Hongwei, Lei Hongjun, Tong Wenbin, Shi Lili, Chen Huiru
College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450011, PR China.
College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, Henan, 450011, PR China.
J Environ Manage. 2023 Oct 15;344:118537. doi: 10.1016/j.jenvman.2023.118537. Epub 2023 Jul 3.
Straw returning is a sustainable way to utilize agricultural solid waste resources. However, incomplete decomposition of straw will cause harm to crop growth and soil quality. Currently, there is a lack of technology to timely monitor the rate of straw decomposition. Dissolved organic matter (DOM) is the most active organic matter in soil and straw is mainly immersed in the soil in the form of DOM. In order to formulate reasonable straw returning management measures , a timely monitoring method of straw decomposition rate was developed in the study. Three water treatment (60%-65%, 70%-75% and 80%-85% maximum field capacity) and two fertilizer (organic fertilizer and chemical fertilizer) were set up in the management of straw returning to the field. Litterbag method was used to monitor the weight loss rate of straw decomposition under different water and fertilizer conditions in strawberry growth stage. The changes of DOM components were determined by three-dimensional fluorescence spectroscopy (3D-EEM). From the faster decomposition period to the slower decomposition period, the main components of DOM changed from protein-like components to humus-like components. At the end of the experiment, the relative content of humus-like components under the treatment of organic fertilizer and moderate water was the highest. Convolutional neural network (CNN) combined with 3D-EEM was used to identify the decomposition speed of straw. The classification precision of neural network validation set and test are 85.7% and 81.2%, respectively. In order to predict the decomposition rate of straw under different water and fertilizer conditions, 3D-EEM data of DOM were used as the input of CNN, parallel factor analysis (PARAFAC) and fluorescence region integral (FRI), and dissolved organic carbon data were used as the input of dissolved organic carbon linear prediction. The prediction model based on CNN had the best effect (R = 0.987). The results show that this method can effectively identify the spectral characteristics and predict the decomposition rate of straw under different conditions of water and fertilizer, which is helpful to promote the efficient decomposition of straw.
秸秆还田是一种可持续利用农业固体废物资源的方式。然而，秸秆不完全分解会对作物生长和土壤质量造成危害。目前，缺乏及时监测秸秆分解速率的技术。溶解性有机物（DOM）是土壤中最活跃的有机物，秸秆主要以DOM的形式浸入土壤。为了制定合理的秸秆还田管理措施，本研究开发了一种秸秆分解速率的及时监测方法。在秸秆还田管理中设置了三种水分处理（田间持水量的60%-65%、70%-75%和80%-85%）和两种肥料（有机肥和化肥）。采用尼龙网袋法监测草莓生长阶段不同水分和肥料条件下秸秆分解的失重率。通过三维荧光光谱（3D-EEM）测定DOM组分的变化。从快速分解期到缓慢分解期，DOM的主要成分从类蛋白组分变为类腐殖质组分。在试验结束时，有机肥和适度水分处理下类腐殖质组分的相对含量最高。利用卷积神经网络（CNN）结合3D-EEM识别秸秆的分解速度。神经网络验证集和测试集的分类精度分别为85.7%和81.2%。为了预测不同水分和肥料条件下秸秆的分解速率，将DOM的3D-EEM数据作为CNN、平行因子分析（PARAFAC）和荧光区域积分（FRI）的输入，将溶解性有机碳数据作为溶解性有机碳线性预测的输入。基于CNN的预测模型效果最佳（R = 0.987）。结果表明，该方法能有效识别光谱特征并预测不同水肥条件下秸秆的分解速率，有助于促进秸秆的高效分解。
