Impacts of Rice Field Winter Planting on Soil Organic Carbon and Carbon Management Index

To tackle with the problem of prevailing farmland abandonment in winter, 5 treatments includes Chinese milk vetch-double cropping rice (CRR), rape-double cropping rice (RRR), garlic-double cropping rice (GRR), winter crop multiple cropping rotation (ROT), winter fallow control (WRR) were set up. By measuring soil total organic carbon, active organic carbon and its components and calculating the soil carbon pool management index in 0~15 cm and 15~30 cm soil layers in the early and late rice ripening stage. The effects of different winter planting patterns on the changes of soil organic carbon and carbon pool management index were discussed. In order to provide theoretical basis for the optimization and adjustment of winter planting pattern of double cropping rice field in the middle reaches of Yangtze River. The results showed that soil total organic carbon, active organic carbon and its components in different winter cropping patterns were increased, and ROT and CRR treatments were more beneficial to the accumulation of soil total organic carbon, active organic carbon and its components as well as the improvement of soil carbon pool management index, which should be preferred in the adjustment of cropping patterns.

Utilization of adaptive neuro-fuzzy interference system and functional network in prediction of total organic carbon content

This paper presents the application of two artificial intelligence (AI) approaches in the prediction of total organic carbon content (TOC) in Devonian Duvernay shale. To develop and test the models, around 1250 data points from three wells were used. Each point comprises TOC value with corresponding spectral and conventional well logs. The tested AI techniques are adaptive neuro-fuzzy interference system (ANFIS) and functional network (FN) which their predictions are compared to existing empirical correlations. Out of these two methods, ANFIS yielded the best outcomes with 0.98, 0.90, and 0.95 correlation coefficients (R) in training, testing, and validation respectively, and the average errors ranged between 7 and 18%. In contrast, the empirical correlations resulted in R values less than 0.85 and average errors greater than 20%. Out of eight inputs, gamma ray was found to have the most significant impact on TOC prediction. In comparison to the experimental procedures, AI-based models produces continuous TOC profiles with good prediction accuracy. The intelligent models are developed from preexisting data which saves time and costs. Article highlights In contrast to existing empirical correlation, the AI-based models yielded more accurate TOC predictions. Out of the two AI methods used in this article, ANFIS generated the best estimations in all datasets that have been tested. The reported outcomes show the reliability of the presented models to determine TOC for Devonian shale.