Machine learning in the evaluation and prediction models of biochar application: A review.

This article reviews recent studies applying machine learning (ML) approaches to biochar applications. We first briefly introduce the general biochar production process. Various aspects are contained, including the biochar application in the elimination of heavy metals and/or organic compounds and the biochar application in environmental and economic scopes, for instance, food security, energy, and carbon emission. The utilization of ML methods, including ANN, RF, and NN, plays a vital role in evaluating and predicting the efficiency of biochar absorption. It has been proved that ML methods can validly predict the adsorption effectiveness of biochar for water heavy metals with higher accuracy. Moreover, the literature proposed a comprehensive data-driven model to forecast biochar yield and compositions under various biomass input feedstock and different pyrolysis criteria. They said a 12.7% improvement in prediction accuracy compared to the existing literature. However, it might need further optimization in this direction. In summary, this review concludes increasing studies that a well-trained ML method can sufficiently reduce the number of experiment trials and working times associated with higher prediction accuracy. Moreover, further studies on ML applications are needed to optimize the trade-off between biochar yield and its composition.

[RAPD analysis for the genetic diversity of Brassica rapa in Tibet].

Tibet, a most beautiful place, locating in southwestern China. She has been called as the third pole of the earth. Unique geological history, complex land surface and climatic zones, various soil types, all different wild vegetations etc., all of these make Tibet a very typical area of vertical agricultural ecosystem. The ecosystem in Tibet may be the most complex in the world, which varies from place to place. Genetic differentiation of 107 accessions of Brassica rapa from Tibet plateau was studied by DNA PAPD analysis using 2210 bp random primers, the genetical distribution in 107 accession of Brassica rapa from Tibet plateau was found. The results are as follows: (1) Total 236 bands were produced from 107 Tibet oilseed accession of B. rapa germaplasm resource in Tibet, of 210 bands amplified from B. rapa germaplasm resource showed polymorphism, with the ratio 88.98%. The result showed that oilseed accession of B. rapa in Tibet has richer genetic diversity; (2) Dendrogram constructed from DNA RAPDs showed that 107 accessions of B. rapa from Tibet plateau were divided into 11 cluster by calculating genetic distance, the cluster analysis showed that the genetic variation among oilseed accessions of B. rapa was closely related with their eco-geographic distribution; extensive variation existed among the accessions from Tibet Province. Based on the analysis of unique geological history, complex land surface and climatic zones, various soil types, complex growing environments, long agricultural history, different cropping systems, and natural and artificial selection as well as plant geography, plant evolution theory, it concludes that Tibet is one of the oil seed gene centers in the word.