Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amount of anomalous data, which can interfere with data analysis, affect the construction of spatial interpolation and prediction models, and then affect the accuracy of nutrient management decisions. The traditional method of identifying soil total nitrogen anomalies based on boxplots suffers from the problems of not being able to identify local anomalies, which can easily lead to misclassification of soil total nitrogen data anomalies, and the detection efficiency is not high. We propose a method to identify soil total nitrogen outliers by combining the Isolation Forest algorithm and local spatial autocorrelation analysis, which can simultaneously detect global and local outliers from large amounts of data and combine organic matter as an auxiliary indicator in the spatial analysis to help judge local outliers. Finally, the results of global and local anomalies were combined to provide a comprehensive assessment of the soil nitrogen data, avoiding the misjudgement or omission of judgement that can occur when using a single method. Using 25,930 soil test data from Yunnan Province in 2009 as an example, we compared and analysed the typical boxplot method and the unsupervised OneClassSVM method and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The results show that the proposed method has a correct detection rate (TR) of 99.97%, a false positive rate (FPR) of 8.06% and a false negative rate (FNR) of 0.01% on the data, which shows high validity and accuracy; it is also comparable to the independent isolated forests (FNR = 4.76%), boxplot (FNR = 3.90%) and OneClassSVM (FNR = 4.77%), and the false negative rate is reduced by 4.75%, 3.89% and 4.76%, respectively.

The Yunling cattle breed, a three-breed crossbreed, which comprises 50% Brahman cattle, 25% Murray Grey cattle and 25% Yunnan Yellow cattle, has several advantageous traits, including rapid growth, superior meat quality, ability to improve tolerance in hot and humid climates, tick resistance and rough feed. It can be rightfully stated that Yunling cattle serve as vital genetic repositories of the local Yunnan cattle. Gaining insights into the genetic information of Yunling cattle plays a significant role in the formulation of sound breeding strategies for this breed, safeguarding genetic resources and mitigating the risks associated with inbreeding depression. In this study, we constructed the Yunling cattle standard reference genome and aligned the whole genomes of 129 Yunling cattle individuals to the constructed reference genome to estimate the current genetic status of Yunling cattle in Yunnan Province, China. The average alignment rate and the average percentage of properly paired are both 99.72%. The average nucleotide diversity in Yunling cattle is 0.000166, which indicates a lower level of diversity. Population structure analysis classified Yunling cattle into two subgroups. Inbreeding analysis revealed that inbreeding events did occur in the Yunling cattle, which may have contributed to the low genetic diversity observed. This study presents a comprehensive assessment of the genetic structure and diversity among the Yunling cattle and provides a theoretical foundation for the preservation and exploitation of these precious germplasm resources.

Yunling cattle is a new breed of beef cattle bred in Yunnan Province, China, which has the advantages of fast growth, excellent meat quality, improved tolerance ability, and important landscape value. Copy number variation (CNV) is a significant source of gene structural variation and plays a crucial role in evolution and phenotypic diversity. Based on the latest reference genome ARS-UCD2.0, this study analyzed the genome-wide distribution of CNVs in Yunling cattle using short-read whole-genome sequencing data (n = 129) and single-molecule long-read sequencing data (n = 1), and a total of 16,507 CNVs were detected. After merging CNVs with overlapping genomic positions, 3,728 CNV regions (CNVRs) were obtained, accounting for 0.61% of the reference genome. The functional analysis indicated significant enrichment of CNVRs in 96 GO terms and 57 KEGG pathways, primarily related to cell adhesion, signal transduction, neuromodulation, and nutritional metabolism. Additionally, 111 CNVRs overlapped with 76 quantitative trait loci (QTLs), including Subcutaneous fat thickness QTL, Longissimus muscle area QTL, and Marbling score QTL. Several CNVR-overlapping genes, including BZW1, AOX1, and LOC100138449, overlap with regions associated with meat color and quality QTLs. Furthermore, Vst analysis showed that PSMB4, ERICH1, SMC2, and PPP4R3A were highly divergent between Yunling and Brahman cattle. In summary, we have constructed the genomic CNV map of Yunling cattle for the first time using whole-genome resequencing. This provides valuable genetic variation resources for the study of the Yunling cattle genome and contributes to the study of economic traits in Yunling cattle.

Yunling cattle is a new breed of beef cattle bred in Yunnan Province, China. It is bred by crossing the Brahman, the Murray Grey and the Yunnan Yellow cattle. Yunling cattle can adapt to the tropical and subtropical climate environment, and has good reproductive ability and growth speed under high temperature and high humidity conditions, it also has strong resistance to internal and external parasites and with good beef performance. In this study, we generated a high-quality chromosome-level genome assembly of a male Yunling cattle using a combination of short reads sequencing, PacBio HiFi sequencing and Hi-C scaffolding technologies. The genome assembly(3.09 Gb) is anchored to 31 chromosomes(29 autosomes plus one X and Y), with a contig N50 of 35.97 Mb and a scaffold N50 of 112.01 Mb. It contains 1.62 Gb of repetitive sequences and 20,660 protein-coding genes. This first construction of the Yunling cattle genome provides a valuable genetic resource that will facilitate further study of the genetic diversity of bovine species and accelerate Yunling cattle breeding efforts.