A genotyping by sequencing (GBS) approach was used to analyze the organization of genetic diversity in V. pubescens and V. chilensis. GBS identified 4675 and 4451 SNPs/INDELs in two papaya species. The cultivated orchards of V. pubescens exhibited scarce genetic diversity and low but significant genetic differentiation. The neutrality test yielded a negative and significant result, suggesting that V. pubescens suffered a selective sweep or a rapid expansion after a bottleneck during domestication. In contrast, V. chilensis exhibited a high level of genetic diversity. The genetic differentiation among the populations was slight, but it was possible to distinguish the two genetic groups. The neutrality test indicated no evidence that natural selection and genetic drift affect the natural population of V. chilensis. Using the Carica papaya genome as a reference, we identified critical SNPs/INDELs associated with putative genes. Most of the identified genes are related to stress responses  and vegetative and reproductive development. These results will be helpful for future breeding and conservation programs of the Caricaceae family. The family Caricaceae belongs to Brassicales, including papaya, mustard, and Arabidopsis thaliana, among other species . CaricacCarica papaya L. is the most successfully cultivated family and is considered to be the fourth most important tropical fruit crop in the world . HoweverVasconcellea pubescens (ex V. cundinamarcensis) is a perennial, herbaceous, and trioecy cross-pollinated species with male, female, and hermaphrodite plants . Its lifV. pubescens is cultivated in several South American countries ,10. In CAnother Vasconcellea species in Chile is V. chilensis (Planch. ex A. DC.) Solms , a small, endemic, deciduous, and succulent shrub with female and hermaphrodite plants (andro-gynodioecy).It grows in semi-arid environmental conditions. Its life cycle starts with the development of its leaves at the beginning of April (autumn), when temperatures reach an average of 10 °C, and it loses them in November when the temperature rises above 20 °C. In springtime (September), the first flowers appear, followed by the fruits, which continue until May of the following year. From November to May, the plant without leaves only maintains flowers and fruits B; the phFemale and hermaphrodite plants present small reddish flowers C. Its frDespite their importance, there have been few genetic studies on Vasconcellea species. The additional analysis of these two species will generate valuable information for future conservation and breeding strategies for the Vasconcellea and Carica genus . Most geNext-generation sequencing approaches (NGSs) have enabled the release of whole-genome reference sequences for many plant species ,33. On tThe use of next-generation sequencing technology has revealed hundreds of thousands of single nucleotide polymorphisms (SNPs), which are the most abundant type of polymorphism found in eukaryotic genomes ,36. UnliA genotyping method based on partial genome sequencing has been developed, which uses restriction enzymes to digest the genome, reducing its complexity; after the DNA digestion, barcoded adapters are linked to the DNA fragments, which are then sequenced by high-throughput methods, obtaining hundreds of thousands of SNPs simultaneously ,38. ThosThe GBS approach is well suited for genetic analysis because it provides useful information for developing conservation and breeding strategies. This information can help to identify priority populations to be conserved, select proper parental lines, and identify advanced breeding lines from the progenies. Additionally, the published reference genome of C. papaya has enabled the analysis of the physical location of the SNPs and INDELs on the genome, showing the relationships of collinearity and synteny between related species ,40. GBS The use of mass sequencing tools and the Carica papaya genome sequence as a reference can be a valuable alternative to identify molecular markers such as SNPs and INDELs in Vasconcellea species. To date, there has been no report describing the SNP/INDEL diversity in any species of the family Caricaceae. Thus, a comparative genomics analysis among related Vasconcellea species would facilitate the identification of biologically relevant polymorphisms to study the evolutionary processes affecting their natural and cultivated populations and enable the development of molecular markers for breeding and conservation programs .This study analyzed the genetic diversity of V. chilensis and V. pubescens using SNPs/INDELs and identified some critical SNPs/INDELs associated with putative genes for future conservation and breeding programs for the Caricaceae family.The C. papaya genome (372 Mb) was used as a reference to identify SNPs/INDELs. In this regard, the GBS had 5 Mb and 4.7 Mb coverage for V. pubescens and V. chilensis, respectively. The sequencing depth was 14X for V. pubescens and 27X for V. chilensis.From 92 samples of V. pubescens, 232,682,246 reads were obtained by GBS. Of them, 89% contained barcode and restriction sites. Finally, 14,213,301 reads were accepted in TOPM. After quality filtering, 4675 SNPs/INDELs were used for genetic analysis .IS value was significant (Fis = 0.50 standard error [se] = 0.006), indicating deviation of the Hardy–Weinberg equilibrium and heterozygous deficits; additionally, the average selfing rate (s) was high (s = 0.67), suggesting a high level of inbreeding.Low levels of polymorphic loci (p = 62% [se] = 1.12%) and heterozygosity (0.04) were found in cultivated V. pubescens . The avep < 0.001).According to the analysis of molecular variance (AMOVA), most of the SNPs/INDELs were distributed among the studied individuals (97%), and the genetic differentiation was low among orchards  was estimated, but a moderate level of heterozygosity (0.12) was observed (IS) and selfing rate were 0.18 and 0.31, respectively. The nucleotide diversity per site (π) was 0.0154, and it was similar between populations. Tajima’s D neutrality test showed a negative value (D = −1.065) and was not significantly different from zero according to the beta distribution at 95% confidence.In analysis . In V. cobserved . Additiop < 0.001).The SNPs/INDELs were mainly distributed among individuals (98%); only a 2.6% genetic differentiation was observed between the populations . SNPs/INDELs were selected when they reached ϕPT values equal or superior to 0.15 after 1000 permutations (p < 0.001). The AMOVA identified 58 SNPs/INDELs (p <0.001) of the genetic differences between V. pubescens and V. chilensis. The Bayesian analysis and Evanno methodology for those 58 SNPs/INDELs indicated that the best estimation of delta K was reached when the K value was equal to 2 (delta K = 430.049 for k = 2), clearly separating both Vasconcellea species  was used as a reference to locate the 58 SNPs/INDELs over the exon, intron, and intergenic regions. Most of the SNPs/INDELs (72%) were positioned over exons, 17% were over introns, and only 11% were located in intergenic regions  between both species. Out of the 58 SNPs/INDELs, 52 were positioned over 30 genes according to the Carica genome. Some of those SNPs/INDELs reported extreme genetic differentiation values  between V. pubescens and V. chilensis: tubulin alpha-2 chain (ϕPT = 0.91); phosphatidyl inositol monophosphate 5 kinase (ϕPT = 0.91); nucleoporin 156 (ϕPT = 0.89); cyclic nucleotide-regulated ion channel family protein (ϕPT = 0.88); remorin family protein (ϕPT = 0.84); autoinhibited Ca2+-ATPase 1 and 2 (ϕPT = 0.77); signal transduction histidine kinase; hybrid-type, ethylene sensor (ϕPT = 0.73); S-adenosyl-L-methionine-dependent methyltransferase superfamily protein (ϕPT = 0.70); nucleoporin 155 (ϕPT = 0.67); DNA repair (Rad51) family protein (ϕPT = 0.62); sec7 domain-containing protein (ϕPT = 0.61); Deoxyxylulose-5-phosphate synthase (ϕPT = 0.57); and disease resistance-responsive (dirigent-like protein) family protein (ϕPT = 0.52).The  regions . The hetV. pubescens were compared, 33.4%  of the genetic differentiation was determined by 28 SNPs/INDELs  of the genetic differentiation among four natural populations. The Bayesian clustering distinguished two groups , one to several hundred base-pair insertions or deletions (INDELs), and variations in the number of tandem repeats . Although the SSRs are considered a relevant DNA variation for genetic analysis , the SNPV. chilensis and V. pubescens using SNPs/INDELs obtained by GBS. In this regard, it was possible to identify 4451 to 4675 high-quality SNPs/INDELs with a depth of coverage of 14X (5 Mb) and 27X (4.7 Mb). We found low levels of genetic variation in these two Vasconcellea species, which contrasts with the estimation of genetic diversity reported for plant species with similar biological characteristics such as autogamy, allogamy, and seed dispersal by gravity or ingestion [To the best of our knowledge, our study is the first report on the organization of genomic diversity in ngestion .V. pubescens showed a high inbreeding level (Fis = 0.50), and the selfing rate (s = 0.67) suggested assortative mating. The scarce gene diversity for this species had previously been reported using 114 inter-simple sequence repeat loci (ISSR [The cultivated ci (ISSR ). The heci (ISSR ) and grate s = 0. suggeste−3) was similar to those reported for apple (π = 3.7 × 10−3 [−3 [−3 [−3 [−3 [Thymus vulgaris (π = 3.1 × 10−3 [The nucleotide diversity (π = 3.2 × 107 × 10−3 ), peach 10−3 [−3 ), grape  [−3 [−3 ), Chrysa [−3 [−3 ), Loblol [−3 [−3 ), and Th1 × 10−3 ).V. pubescens was superior to that found for Carica papaya .V. pubescens in Chile [The domestication process (selection and seed propagation) may have played an essential role in reducing the genetic diversity for in Chile , and it V. pubescens) has been cultivated in northern Chile since the 16th century [V. pubescens is self-compatible and displays both cross-pollination and self-pollination simultaneously [The highland papaya ( century . Interesaneously .p < 0.001) can be explained because seedlings established in the southern orchards represent a small portion of the genetic diversity of the northern orchards, and the selection practices applied by the growers may have increased the genetic differentiation between them. These results are coincident with those previously reported [V. pubescens has been affected by a selective sweep and population expansion after a bottleneck during its domestication [The genetic differentiation observed between northern and southern orchards  and nucleotide diversity (p = 15.4 × 10−3) than cultivated V. pubescens but also a lower inbreeding coefficient (Fis = 0.18) and selfing rate (s = 0.3).In contrast, the natural populations of p < 0.001). The Bayesian analysis identified two genetic groups (K = 2), including individuals from the four analyzed populations. The Mantel test (r(AB) = 0,32 p = 0.05) indicated that there is a slight relationship between the geographic location of the populations and the pattern of genetic diversity. The Tajima D neutrality test (D = −1.065) was negative but not significant, suggesting that there is no evidence that natural selection and genetic drift can affect genetic diversity.The genetic differentiation among populations was low but significant  and northern populations . Therefore, the pattern of genetic diversity in V. chilensis may reflect the outcome of its natural history; thus, the current fragmentation of its populations may reflect recent events that have not yet affected its genetic structure.From a conservation point of view, the populations located in the “Valle del Encanto” and “Fray Jorge” are protected because the former are in a sector declared a National Historic Monument, and the latter are within a National Park. However, the “Huachalalume” and “Chungungo” populations are not protected and thus suffer from intense anthropogenic pressure.C. papaya genome. Several genes were found to be associated with abiotic and biotic stress responses and organogenesis. At the interspecific level, 58 SNPs/INDEL explained 53% of the genetic differentiation between V. pubescens and V. chilensis, of which 90% were tagged over exons and introns of 30 genes, most of them involved in vegetative and reproductive processes associated with adaptations to different stress conditions , which showed high genetic differentiation. This exon was located in the putative gene CK203 (42% homology), which responds to stress conditions mediated by abscisic acid [Most of the genetic differentiation among populations of sic acid .V. chilensis, only two variants were found over coding regions (PAC:16403969 and PAC:16403933 CDS.1); both showed a high level of genetic differentiation and were located in the gene RING/U-box superfamily protein, which encodes a novel E3 ubiquitin ligase that acts as a central negative regulator of floral organ size [Similarly, for gan size .V. pubescens and V. chilensis have certainly been subjected to different events during their natural history. Domestication, selection, genetic drift, and inbreeding have affected the genetic structure of both species in different ways. Most of the genetic differentiation among populations has been observed in intergenic regions, probably associated with the control of gene expression, although it is not possible to rule out random events.V. pubescens and wild V. chilensis to identify genetic polymorphisms (SNPs/INDELs) associated with interpopulation and interspecific differences between these two papaya species. Further research could focus on the isolation of these genes reported here; moreover, screening of their expression patterns will provide valuable information to carry out future conservation and genomics-assisted breeding plans.In this study, we used GBS to understand the genomic diversity and population structure of the cultivated V. pubescens and V. chilensis, respectively. The samples were frozen in liquid nitrogen and stored at −80 °C until DNA extraction.Young leaves were collected from 92 and 94 adult plants of V. pubescens were collected in the Coquimbo Region ) [http://bio-bwa.sourceforge.net/, accessed on 22 April 2021, [https://www.maizegenetics.net/tassel/docs/TasselPipelineGBS.pdf, accessed on 22 April 2021, [GBS protocols were carried out in the Institute of Biotechnology, Cornell University, Ithaca, NY, USA (e 2022)) . Librarie 2022))  using thil 2021, ). A ‘masil 2021, ,91).https://biology-assets.anu.edu.au/GenAlEx/Download.html, accessed on 15 May 2022, [https://sites.ualberta.ca/~fyeh/popgene.html, accessed on 3 May 2021, [IS/(1 + FIS) × 100 [SNPs/INDELs were identified following the nomenclature defined in the papaya genome v1.0 . Heterozay 2022,  and Popgay 2021, ). The seS) × 100 .http://www.ub.edu/dnasp/, accessed on 3 May 2021, [https://www.megasoftware.net/, accessed on 3 May 2021, [The nucleotide diversity (π) values were estimated as the mean pairwise difference between two sequences for all possible sequence combinations . The expay 2021, ), and Meay 2021, ). The coay 2021, .Tajima’s D neutrality test (D) was used to explore the effect of selection and genetic drift on nucleotide diversity ,97. The https://web.stanford.edu/group/pritchardlab/structure.html, accessed on 3 May 2021, [https://taylor0.biology.ucla.edu/structureHarvester/, accessed on 3 May 2021, [http://www.royfrancis.com/pophelper/articles/index.html, accessed on 3 May 2021, [http://omicsspeaks.com/strplot2/, accessed on 3 May 2021, [The population structure was inferred by a Bayesian model clustering algorithm using Structure v.2.3.3 . Five iay 2021, . The proay 2021, ) and Popay 2021, ) were usay 2021, .V. pubescens and V. chilensis and among V. pubescens and V. chilensis populations were estimated using AMOVA ϕPT. The ϕPT estimates the proportion of the variance among populations relative to the total variance [p) and missing data per locus inferior to 0.10. The ϕPT and its significance level for each locus were estimated with 1000 permutations through Genealex v6.5.The SNPs/INDELs that enable differentiation between variance . The criCarica papaya genome, the selected SNPs/INDELs were used to identify its physical positions and putative genes. The location of each SNPs/INDEL within the exon and intron was determined using a Perl script (www.perl.org (accessed on 10 September 2021), determining the gene structure limits from the Carica papaya genome GFF annotation, version ASGPBv0.4 [According to the SGPBv0.4 .V. pubescens and V. chilensis, respectively. The cultivated V. pubescens had a reduced genetic diversity at the genomic level and small differentiation between the cultivated populations, although it was possible to distinguish slight differences in the population from southern Chile . The effect of a population bottleneck during domestication in Chile could explain those results. Therefore, it is necessary to introduce new germplasms from their centers of origin and initiate a breeding program to develop varieties that combine the adaptation of local genotypes cultivated for hundreds of years in Chile with genotypes from Colombia and Ecuador. On the other hand, the fragmented and isolated natural populations of V. chilensis had a moderate genetic diversity and low differentiation between populations, although it was possible to appreciate a particular trend associated with their geographic distribution. Its adaptations to the semi-desert conditions of northern Chile  and temperatures that fluctuate between 9 °C and 20 °C make this species a unique and valuable genetic resource. Finally, it is urgent to take measures to conserve its natural populations because they are suffering based on its lower number of plants due to human activities.GBS technology enabled the identification of 4675 and 4451 SNPs/INDELs in two papaya species:


Coffea arabica and Coffea canephora. The latter is a self-incompatible plant with high natural genetic variability. The detection of polymorphism at the DNA level by molecular markers allowed significant progress with the selection of superior plants. The objective of this study was the molecular characterization of C. canephora using microsatellite markers. To this end, a population of forty-four C. canephora genotypes and one C. arabica genotype, was evaluated with 21 primers. These primers identified 61 alleles in the population and between 2 and 5 alleles per locus. The information index indicated a high level of polymorphism of the analyzed markers. According to the observed and expected heterozygosity, the genetic diversity in the population is high. The overall inbreeding coefficient of the population detected high heterozygosity and zero inbreeding within this population. Genetic diversity among the accessions was also evaluated by the unweighted pair-group method based on arithmetic averages (UPGMA). Six groups were formed based on Mojena’s cutting rule and three using the Bayesian approach. These results confirmed the existence of genetic diversity, genetic variability and a potential for selection in future breeding efforts involving the 45 genotypes studied.The genus Coffea comprises more than 100 species, of which the most commercially important are  C. arabica L. and C. canephora Pierre ex A. Froehner account for approximately 99% of the global coffee bean production. Brazil is the world’s largest producer and second largest consumer of coffee. The production and demand of C. canephora has increased significantly due to its use in blends with C. arabica, to produce espresso and soluble coffee, owing to the high soluble solids content, high industrial yield and contribution to a full-bodied coffee [Of the more than 100 species of the genus Coffea, C. canephora is an allogamous species [In view of the gametophytic self-incompatibility,  species  with hig species ,4. In br species .In breeding programs in the past, plants were selected based on morphological markers, which are modified by environmental influence . With thC. canephora breeding in the context of targeted high-throughput genotyping [These markers are already applied in studies on notyping , geneticnotyping  or to evnotyping , populatnotyping .C. canephora must consist of a combination of genetically divergent genotypes to improve pollination efficiency and ensure successful crop production. In this sense, genetic diversity is essential to maintain productivity, quality and also raise the capacity of response to biotic and abiotic stresses [C. canephora clones is fundamental in the planning of recombination strategies between genotypes and the composition of new cultivars, and SSR markers are an effective technique for these purposes. We raised the hypothesis that molecular markers such as SSR are able to detect differences at the DNA level allowing the distinction of genotypes without environmental influence.Clonal cultivars of stresses . Thus, tThus, the objectives of this research consisted of: (i) estimation of the genetic distance between plants or clones; (ii) genetic characterization, to measure and structure the genetic variability between genotypes; and (iii) identification of divergent plants for use as parents in coffee breeding programs.C. canephora population and thus analyze the molecular genetic diversity.For this study, a total of 48 primers between SSR and EST were tested to identify the most divergent plants within this In the analysis of genetic diversity of the study population, 21 of the 48 tested primers produced amplified DNA fragments, which were selected for being polymorphic.C. arabica accessions from several areas of the Philippines conserved in an ex situ collection, 19 SSR markers were used, 15 of which were polymorphic. The markers used in this study were also highly polymorphic, i.e., they can be used to detect differences between genotypes based on their genetic relationships.In evaluation studies by Baltazar et al. , on the The 21 primers detected 61 alleles in the population and 2 to 5 alleles per locus. Five alleles were detected at SSR-463 and SSR-1005, indicating greater diversity of these than of the other loci. Averaged across the entire data set, the study population had 2.9 alleles . This loHowever, the estimated allele frequency is strongly influenced by the number of plants in the population and tends to increase with increasing sample size. The reason is that in large samples, the chance of detecting rare, i.e., low-frequency alleles in the population is greater ,16.C. canephora in Uganda, a mean of 4.7 alleles per locus in native populations was found. In comparison, a greater number of alleles was detected in the collections of cultivated genotypes. The relatively low allelic diversity found in the native population may be due to restricted gene flow between the closest populations, isolation by fragmentation, genetic drift, anthropogenic disturbance in those forests or mortality due to extreme stress conditions. These results were expected, since different populations and germplasm collections were analyzed in the above studies, while in this study only one clonal C. canephora population was evaluated, which explains the low number of alleles it contains.In studies by Kiwuka et al. , on the C. canephora, C. excelsa and C. liberica). These authors also evaluated 46 Arabica and 3 non-Arabica coffee genotypes and concluded that the mean number of alleles was influenced by the number of samples, their genetic base, number of microsatellites used and polymorphism level. This result is similar to ours, as the low allele frequency found in this study may be due to the low number of genotypes in the studied population.In a study of digital DNA screening of microsatellite markers in Coffea sp germplasm, conserved in Costa Rica, Sanchéz et al. , found aC. arabica genotypes and three diploid Coffea species  from Brazil and Ethiopia, using 30 SSR markers, Da Silva et al. [In an analysis of the population structure and genetic relationships between 33 a et al. , identif2 and the mean across all loci was 0.58  of 12 loci (57%) exceeded 0.5. The highest value was 1.038 for locus SSR-845, the lowest 0.184 for locus SSR-55was 0.58 .Studies by Botstein et al. , proposeC. canephora and one C. arabica genotype. The index can be affected by the number of alleles and allele frequency at the locus.The genetic diversity of Arabica coffee in the Philippines was analyzed using SSR markers by Baltazar et al. , with a The observed heterozygosity (Ho) ranged from 0.093 at locus SSR-1005 to 1 at locus SSR-744 (mean of 0.359). The observed heterozygosity is a measure of the frequency of heterozygotes in a studied sample and indicates the existence of genetic variation in the population, since each heterozygote has different alleles for a given gene, and the frequency of heterozygotes tends to be higher with higher genetic diversity.2 to 0.588 at locus SSR-081 (mean of 0.34).According to Hardy–Weinberg expectations, the expected heterozygosity (He) ranged from 0.073 at SSR-55The observed heterozygosity was higher than expected at 17 loci (80%). When the Ho is greater than expected, this may suggest an excess of heterozygotes in the population in relation to the Hardy–Weinberg equilibrium model, De Moura et al. . In the C. canephora in Upper Guinea, Labouisse et al. [In a study on the genetic structure and diversity of e et al. , found gAntioxidant activity and stable free radicals in Robusta green coffee genotypes were analyzed by  and the Analyzing the values of the fixation index (F) or inbreeding coefficient of the study population, the highest F values (0.947 and 0.522) were found at loci SSR-353 and SSR-1005. However, most values were lower and negative at 14 loci (66%) and the overall population mean was −0.035 .The fixation index is one of the most important parameters in population genetics, as it measures the balance between homozygotes and heterozygotes in populations, which can vary from −1 to +1. Values close to zero indicate random mating, negative values indicate excess heterozygosity and zero inbreeding for that locus in the study population, while high positive values indicate high inbreeding , causingAccording to our results, the fixation index at one locus was very close to 1 (0.947). This may be related to the absence of alleles at that locus, caused by failure in the amplification of alleles, the occurrence of null alleles, or may also indicate the presence of inbreeding. According to Cruz et al. , the maiAlso, with regard to the fixation index, most values found were lower and negative, indicating heterozygosity excess at these loci. The general population mean was −0.035, i.e., zero inbreeding for this population. Another factor is that at the loci with negative values, heterozygosity was also greater than expected, which indicates that the alleles at these loci were not being fixed by inbreeding.C. canephora receives pollen from several other coffee plants in its surroundings, maintaining the high heterozygosity and zero inbreeding, thus minimizing the risks of genetic drift and allele loss in the population. This result is extremely interesting from the point of view of productivity, showing that the establishment of homogeneous progenies for productivity and quality-related traits may be expected. Concomitantly, diversity is expected among the progenies, so that the best can be selected and cloned to ensure productivity and quality of the final product.These data showed high rates of cross-pollination in the species under study as well as wide genetic diversity among the analyzed clones. Due to being a self-incompatible species, Obvious kinship (K) was not detected in most individuals in this population . An apprThe genetic diversity among the accessions was evaluated using the average group linkage method (UPGMA), with the stopping rule of , resultiIn genetic diversity studies with microsatellite markers, the most commonly used clustering method is UPGMA, which maximizes the cophenetic correlation coefficient and improves data reliability.In this analysis, the cophenetic correlation index was 0.83, indicating a strong correlation between the dissimilarity matrix and the cophenetic distance matrix computed from the dendrogram. The cophenetic correlation estimate varies from 0 to 1; the higher this coefficient, the greater the representativeness of the dendrogram in relation to the genetic distance matrix. The diversity was confirmed by the formation of groups containing similar genotypes in the cluster analysis using the hierarchical UPGMA method .Group I contained the largest number of plants, with 16 (35.55%). Group II contained 13 plants (28.88%), group III, 8 (17.77%), group IV, 6 (13.33%) and groups V and VI, 1 plant each (2.22% each). This amount of genotypes in the same group indicates that these plants share the highest number of alleles for the evaluated loci.C. arabica genotype formed the fifth and sixth groups, respectively, with such high variability that they did not fit into any of the other groups. Groups with only one genotype were the most divergent in relation to the other groups. It is worth mentioning that both were used as a type of control. Genotype C. arabica was included in this study as control, and it was expected to have divergent genetic characteristics isolated from the C. canephora genotypes. Moreover, the fact that C. arabica formed an isolated group can confirm the effectiveness of the SSR–EST primers that are also used to distinguish different species of the genus Coffea.The seed-derived genotype and C. canephora populations is essential for the development of new cultivars of this crop. By selecting the most dissimilar genotypes for future crosses in breeding programs, the problems of genetic self-incompatibility in the species can be avoided.The detection of divergent genotypes in genetically distinct heterotic groups of The most genetically dissimilar genotypes, 122 and Arabica (0.71), had 13 alleles in common, at 16 of the 21 analyzed loci, although in this study Arabica was not used in crosses, but as a control.Genotype Emcapa 02 was most dissimilar in relation to some genotypes, e.g., Z40, 700 and P1, with dissimilarity measures of 0.64, 0.63 and 0.61, respectively, and 16, 15 and 16 alleles in common.The dissimilarity between the genotypes Peneirão and 122 and between Ouro Negro 2 and AT was 0.63 and 0.61, respectively; the first two had 16 and the latter 19 alleles in common.−1 for Emcapa 02, 74.55 bags ha−1 for genotype Z40 and 89.19 bags ha−1 for genotype 700. The productivity of genotypes Peneirão and 122 was 81.07 and 99.22 bags ha−1, and genotypes Ouro Negro 2 and AT produced 74.70 and 128.93 bags ha−1, respectively. Genotypes 700 (Group III) and AT (Group IV) were not only dissimilar and highly productive, but also have an exceptionally low water requirement for mature coffee (less than 315 L) to yield one 60 kg bag of green coffee beans  , A1 (Group II), L80 (Group III) and AT (Group IV) exceeded 100 bags hactively) . These hThe genotypes Emcapa 143 and Tardio V were the least dissimilar, in other words, the closest . Most genotypes had shorter distances, probably due to the way in which these clones had been selected by farmers. In general, these producers all selected for the same agronomic characteristics of coffee trees, addressing high yield, plant height and disease resistance, among others. In this way, variability may have declined, and the clones may be somewhat similar. However, possible crosses will be proposed between plants from different groups, based on the greatest dissimilarity and high productivity.The genotypes within a group are genetically similar, evidencing a certain pattern among most genotypes of the same group. This similarity was observed in groups I, II, III and IV, because in spite of the high genotypic diversity in this population, as mentioned above, some genotypes had a certain degree of genetic similarity and were therefore clustered in the same group.The similarity between these genotypes can be explained by genetic drift that occurred when conilon was first introduced in the country, according to 30.According to Fazuoli et al. , despiteAccording to Cruz et al.  and CarmGenetic divergence analyses can be used to refine the selection of parents to be crossed. Crosses between highly divergent parents can maximize progeny heterozygosity and heterosis, thus increasing the chances of selecting superior elite plants. Clones with greater genetic dissimilarity can be selected for population formation, because by recommending a set of hybrids with greater genetic distances, the problems of genetic self-incompatibility of the species are minimized. In addition to the genetic distance, crosses between genotypes with a lower degree of kinship should be prioritized. As verified in C. canephora genotypes and K = 3 was indicated as the most likely number of clusters , used the alleles for inferences about the genetic structure of clusters . In a Bae et al.  found K An adherence probability of 70% that each genotype belongs to a certain group was assumed. The 45 genotypes evaluated were grouped as follows: 25 genotypes in group I (red), four in group II (green) and 16 in group III (blue) . Some plGenotypes 37, 15, 32, 39, 2, 10, 29, 42, 7, 9, 27, 22, 20, 26, 3, 40, 5 and 28 have 100% adherence to the red group and genotypes 30, 4, 11, 36, 24, 19, 1, 43, 35 and 23 have 100% adherence to the blue group. This organization means that the genotypes in the red/blue/green group have sets of alleles that differentiate them for the set of markers used.The plants 33, 16, 25, 34 and 8 have characteristics of all three groups, that means, less than 70% adherence to any of the three groups. Therefore, some plants are more likely to adhere to the red or the blue group, which could indicate that they share alleles. The SSR markers proved efficient in differentiating the different genotypes from the different locations, according to their genetic variability. The presence of genotypes with mixed probabilities can be explained by the genetic structure of the population, which comprises a small number of genotypes that share alleles with each other, due to their form of reproduction.C. canephora genotypes that belong to different groups, as they are genetically distant. It is important to combine these data with agronomic performance data of these genotypes, to select the plants with the greatest agronomic potential.Using a Bayesian analysis, the groups were distinguished with regard to the analyzed loci and the plants that shared the same analyzed genomic regions were detected. This kind of clustering is promising as an orientation for crosses between C. canephora genotypes, most of which had been selected by coffee growers in the region. Planting occurred in April 2014 in Nova Venécia, a county in northern Espírito Santo, Brazil, on a private property , where the mean annual temperature is 23 °C. The regional climate is tropical, characterized by hot and humid summers and dry winters, classified as Aw by Köppen . The soil of the site was classified as a Latossolo Vermelho-Amarelo, distrófico, with clayey texture and wavy relief [The experiment was carried out in a plantation of 43 y relief .Coffea arabica genotype of a cultivar (Catucaí 2SL) was also used, resulting in a total of 45 genotypes.The genotypes were arranged in a randomized complete block design, with three replications, and each treatment consisted of seven plants of each genotype. The 44 genotypes  were proManagement practices were applied according to the crop-specific technical guidelines and consisted basically of weed control with herbicides and brush cutters, preventive phytosanitary management, liming, fertilization and drip irrigation .Five light green (young) leaves were collected from the apex of each genotype (of one plant per genotype). The samples were immediately wrapped in aluminum foil, labelled and first frozen in liquid nitrogen. The samples were then transported on dry ice to the laboratory and stored at 80 °C until DNA extraction.Genomic DNA was extracted and analyzed at the DNA Marker Sector of the Plant Breeding Laboratory of the Center of Agricultural Science and Technologies, State University of Northern Rio de Janeiro Darcy Ribeiro (LMGV/CCTA/UENF), Campos dos Goytacazes, RJ.The frozen samples were ground with a pestle in crucibles with liquid nitrogen and stored in 2.0 mL microtubes. Genomic DNA was extracted by the method proposed by , with ad−1)  and stained with a mixture of Gel RedTM and Blue Juice (1:1). The samples were photo-documented with the DNR MiniBIS Pro® bio-imaging system. Based on the images, the DNA concentration was estimated in comparison with the 100 bp marker and the DNA samples were diluted to a working concentration of 10 ng/μL−1.After extraction, the DNA was analyzed on 1% agarose gel with 1X TAE buffer , using a 100 bp Lambda (λ) marker , a total of 58 primer pairs were tested and used to amplify SSR and EST loci of eactions .4SO4), 1.5 μL MgCl2 (25 mM), 1.5 μL dNTPs (2 mM), 1 μL primer (R + F) (5 μM) and 0.12 μL Taq-DNA polymerase (5 U/μL) .A 35-cycle PCR amplification was carried out in an  96-Well Thermal Cycler, as follows: 94 °C for 4 min ; 94 °C for 2 min (cyclic denaturation); primer-specific temperature, in °C, for 1 min ; 72 °C for 2 min (cyclic extension); 72 °C for 10 min ; and holding step at 4 °C for unlimited time. The final volume of each sample was 13 μL, containing 2 μL DNA (5 ng/μL), 1.50 μL 10X Buffer , where amplified fragments between 35 and 500 bp long were separated at a resolution of approximately 2 bp. Each run lasted 2 h 20 min at 8 kW.The data of the 21 amplified SSR and EST markers were converted into numerical codes for each allele per locus. This numerical matrix was constructed by assigning values from one to the maximum number of alleles per locus, as described below: one locus with three alleles in homozygous form is represented as 11, 22 and 33  and 12, 13 and 23 for those in heterozygous form . Based on the numerical matrix, three indices were tested: unweighted index, weighted index and the Smouse and Peakall index .(1)∑j−1jp = aj/A: weight associated with locus j, determined by:j: total number of alleles at locus jaA: total number of alleles studiedjc: number of common alleles between accession pairs i and i’.The index represents similarity measures, while for cluster analysis, dissimilarity measures are recommended, defined by:Once the distance matrix was calculated, dendrogram clustering was performed using the UPGMA method (Unweighted Pair-Group Method Average), on Mega software version 6 . The optThe genetic variability among the 45 genotypes was estimated with Genalex 6.5 software , based oTo assess the structure of the 45 genotypes, a method based on Bayesian clustering algorithms was used, on STRUCTURE version 2.3.4 software . To thisThe Δk statistical test was performed using Structure Harvester software and the Evanno criterion . This crI is the number of simulated groups, from i = 1 to i = 10; and ABS is the control module.This Δk value was estimated for each k and the highest Δk selected. After choosing the optimal Δk, the simulation with the lowest LnP(D) value was chosen from a run of 10 simulations. In the resulting graph, each color represents a possible group of structured plants.C. canephora genotypes efficiently and were useful to make headway in breeding of conilon coffee. Genetic variability was confirmed in the evaluated population, which was structured into six distinct groups by UPGMA clustering and into three groups using a Bayesian analysis. Crosses between Bamburral and Z21 (Group I), A1 (Group II), L80 (Group III) and AT (Group IV) individuals are recommended in order to maintain the genetic variability of the population and high productivity. There is no kinship between the indicated genotypes.The SSR markers used in this study discriminated the


Improving the ecological well-being performance (EWP) of natural resources and environmental consumption in relation to human well-being, within the ecological boundary, is necessary for sustainable development. This study used the Super-SBM model to measure the urban EWP of urban agglomeration in the middle reaches of the Yangtze River (MRYRUA) in 2020. The spatial differentiation characteristics of EWP in the MRYRUA were identified. The heterogeneity in the direction and size of the influencing factors of EWP at different urban hierarchy (UH) levels was empirically tested by establishing a threshold model. The results are as follows: (1) In 2020, the EWP of the study area showed a trend of high levels in the southwest and low levels in the northeast. The EWP presented a multi-center “core–periphery” distribution, and the characteristic of “central collapse” was evident. The UH level of the middle and lower hierarchy-level cities was inconsistent with its EWP. (2) A non-single linear relationship was found between the influencing factors of the EWP of the MRYRUA and the EWP. The impacts of technological progress, industrial structure, environmental regulation, and population density on the EWP of the MRYRUA all showed threshold characteristics. (3) Heterogeneity and stages were both observed for the influencing factors of EWP under different UH levels. The effect of technological progress on EWP presented the characteristics of bidirectional and two-stage developments, and environmental regulation presented the features of a significant positive three-stage development. Both industrial structure and population density presented two-stage aspects, but the former acted in a negative direction, while the latter served in a positive order. This study provides a theoretical basis for the government to formulate differentiated regional policies and promote the coordinated improvement of EWP among cities at all hierarchy levels in the urban agglomeration. This study is of great significance to the sustainable development of urban agglomerations. Its results can provide a reference for other urban agglomerations, metropolitan areas, and city clusters worldwide to coordinate economic development, ecological protection, and to improve people’s well-being. The world is facing threats such as resource shortages, environmental pollution, ecological imbalance, climate change, and global poverty. How to coordinate the relationship between resource and environmental consumption, economic growth, and improvement of well-being within the ecological boundary has become a hotspot of global concern in recent years . AccordiEWP refers to the efficiency of converting natural resources and environmental consumption into human well-being ,17. It cIn recent years, the research on EWP mainly focused on three aspects. The first aspect is the empirical measurement of EWP at the national, regional, and city levels. Dietz et al.  used panThe second aspect is research on the regional and spatial differences in EWP. Fang and Xiao  found thThe third aspect is research on the factors influencing EWP. Koziuk et al.  found thAlthough researchers have conducted many explorations around EWP, some areas remain unexplored. (1) Existing studies were often carried out on a large scale, such as at the national, regional, and provincial levels. However, the analysis based on the urban agglomeration level is relatively lacking. Larger spatial scales tend to make researchers ignore the spatial heterogeneity of smaller regions . Urban aThe urban agglomeration in the middle reaches of the Yangtze River (MRYRUA) has played an essential role in the green development process of the Yangtze River Economic Belt . HoweverThe MRYRUA is located in the central region of China. According to the Development Plan for Urban Agglomeration in the Middle Reaches of the Yangtze River , the MRYThe evaluation units were 31 prefecture-level cities in the MRYRUA. The space vector data came from the Resource and Environmental Science and Data Center website of the Chinese Academy of Sciences . The socEWP refers to the efficiency with which natural resources and the ecological environment are transformed into a corresponding level of human well-being. This study referred to relevant studies such as Bian et al. , Yao et In 1933, central place theory first systematically defined the spatial organization and structure of the urban agglomeration . The theThe Super-SBM model has often been used to measure input–output efficiency. Compared with the traditional DEA model, it can solve the slack problem of input and output variables . It can ρ* is the efficiency value of EWP in the MRYRUA; M and N denote the number of input and output variables, respectively; I denotes the number of DMU; ms−, ns+ represent the slack of input and output; mx, ny are input and output vectors; mkx, nky are input and output, respectively; λi is the weight coefficient; and i represents the DMU i. When ρ* ≥ 1, it means that the decision-making unit is entirely effective, and both ms− and ns+ are 0; when ρ* < 1, it means that the efficiency of the DMU is lost.In Formulas (1) and (2), This study adopted the comprehensive index evaluation method in order to evaluate the UH level. First, the range standardization method was used to process the original data in order to eliminate the dimensional differences uniformly. Second, the entropy weight method was used to determine each indicator’s weight objectively. Finally, the total value of each weighted index was calculated. The specific calculation formula is found in related research .iZ  is the EWP value of the city, and  is the plane space coordinate. The trend surface function can be expressed as follows:The trend surface approximates the actual surface, which can simulate the spatial distribution law and variation trend of geographic elements . This stZi  is the trend function, which represents the trend value in an extensive range. This study used a second-order polynomial to measure the trend value of regional EWP; Ti  is the trend function; and εi is the random autocorrelation error, which represents the deviation between the actual value and the trend value of the EWP in the city i.In Formulas (3) and (4): Threshold regression can mine the jumping or abrupt change rules between variables and is often used to study the heterogeneity of interactions between variables . The thriY is the explained variable of the city i. I (•) is the indicative function, which takes the value of 1 when the expression in the brackets is true and the value of 0 when it is false. iD is the core explanatory variable, and iT is the threshold variable. δ is the threshold value, iX is the control variable, and λ is the coefficient of the control variable iX; further, λ0 is the constant term and iε is the random disturbance term. When iT ≤ δ, the core explanatory variable iD’s coefficient is λ1, and when iT > δ, the core explanatory variable iD‘s coefficient is λ2. Formula (5) only assumes one threshold, and the test of two or more thresholds can be deduced.In Formula (5), EWP was graded using the natural breaking point classification method. The spatial distribution of EWP of the MRYRUA  was visuThe EWP of the MRYRUA showed a trend of higher performance in the southwest and lower in the northeast. Further, the multi-center “core–edge” distribution trend was remarkable. At the junction of the three sub-urban agglomerations, the characteristic of “central collapse” was prominent. The UH level of the city did not match the level of EWP. Specifically , Table 3The central cities with high UH levels possessed a higher EWP, therefore showing a more coordinated “resource, environment, and human well-being” relationship. It has also produced a radiation-driving effect on the surrounding cities. However, the effect on more distant cities was not good. The following UH-tiered cities were still in the stage of rapid development. The relationship between its resources and environment and people’s well-being needs further improvement; these cities failed to play the leading role in their corresponding hierarchy level. Severe administrative barriers remained in the MRYRUA, making the EWP’s “collapse area” appear at the junction of sub-urban agglomerations or the border between provinces. Overall, the synergy of green development among cities in the study area was weak. A spatial polarization effect of EWP was observed, resulting in significant urban differences and spatial differentiation characteristics in the EWP. Strengthening the connection between middle and low hierarchy level cities and central cities in the region, improving development quality, enhancing people’s well-being, and improving the EWP, are all necessary.Cities at different UH levels have different natural and social environmental backgrounds. The correlation between EWP and its influencing factors is complex, and the probability of a non-linear relationship is high. To a certain extent, this phenomenon shows the possibility of the threshold effect in regard to the UH level. To explore the heterogeneity of the effects of various influencing factors, studies including those of Li et al. , Hu et aThe first aspect is technological progress. On the one hand, technological innovation helps to promote the organic integration of advanced technology and production processes. It can reduce resource waste and environmental damage in economic activities and increase economic output, ultimately improving EWP. On the other hand, new technologies generated by technological innovation can be divided into two categories: production technology and green technology. The former mainly affects production efficiency, while the latter mainly affects resource consumption and environmental pollution intensity. If more investment is used to develop production technology, it will expand the production scale. It will have a promotion effect on resource consumption and pollution discharge, thereby hindering the improvement of EWP. Therefore, technological progress is expected to have a bidirectional effect on EWP.The second aspect is industrial structure, of which its upgrading reflects the replacement of production factors and the upgrading of production technology. These improvements have a meaningful impact on economic output and pollution emissions. The secondary industry is mainly dominated by industries with the most concentrated resource consumption and environmental pollution, such as the heavy chemical and construction industries. The more significant the proportion of the secondary industry, the more serious the pollution will be, and the lower the EWP. Therefore, the impact of the industrial structure is expected to be negative.The third aspect is environmental regulation. According to the Porter hypothesis , reasonaThe fourth aspect is population density. Population agglomeration can promote the efficient allocation and intensive utilization of resources, stimulate the formation of consumer markets, and promote urban economic development. When population agglomeration produces scale effects, marginal consumption and emission reduction costs can be reduced. Thus, a pollution reduction effect is formed , promotiThe multicollinearity among the indicators was tested by the variance inflation factor (VIF) diagnostic method. As seen in The model took the UH level as the threshold variable, and the threshold test was performed on the core variables in turn . TechnolIn model (1), the impact of scientific and technological progress on EWP presented a two-way and one-threshold feature. When the UH level was lower than 0.221, technological progress negatively, but not significantly, impacted the EWP. When the UH level exceeded the threshold, the elasticity coefficient of technological progress (0.0348) was positive and passed the 5% statistical level test. Five cities, including Wuhan and Changsha, were within the threshold. This result was as expected, confirming the bidirectional effect of technological progress on EWP. This means that for cities at different hierarchy levels, the effect of technological progress on EWP is different. High-level cities had a higher level of development and pursued green and high-quality development. The proportion of green technology investment in scientific and technological innovation was enormous, as such the positive effect of scientific and technological progress was more substantial, which is conducive to EWP. The scale of technology investment in low- and middle-level cities was relatively small; further, forming a scale effect was not easy. The aim of the investment was to undertake more technology transfer from high-level cities , as suchIn model (2), the industrial structure represented by the proportion of secondary industry added value in GDP had a significant negative single-threshold characteristic on the impact of EWP. When the UH level was below 0.059, the elasticity coefficient of industrial structure on the EWP was −0.0466. Seven cities, including Pingxiang and Jingdezhen, were in this range. When the UH level exceeded the threshold, the elasticity coefficient of the industrial structure was −0.0530. Twenty-four cities, including Wuhan and Changsha, were in this range. This result was as expected. The higher the added value of the secondary industry, the more resource consumption and environmental pollution will be produced, harming the environment and EWP. The industrial structure of higher-level cities, therefore, had a more profound impact on EWP.In model (3), the impact of environmental regulation on EWP presented a significant positive double-threshold feature. When the UH level was lower than 0.042, the elasticity coefficient of environmental regulation on EWP was 0.135, and five cities, including Yingtan and Xiantao, were below this threshold. When the UH level was between 0.042 and 0.237, the elasticity coefficient of environmental regulation was reduced to 0.129, and 22 cities, including Hengyang and Yichang, were in this range. When the UH level was higher than 0.237, the elasticity coefficient of environmental regulation was increased to 0.137. Four cities, including Wuhan and Changsha, crossed this threshold. The impact of environmental regulation on EWP presented a U-shaped characteristic with the change in UH level. For low hierarchy level cities, the cost and difficulty of environmental governance were relatively small, and the governance effect was more pronounced. As the UH level increases, the cost of environmental regulation and the difficulty of governance also increase, resulting in inefficient governance. Higher hierarchy level cities pursued the concept of coordinated development of “resources, environment, economy, and society,” and their environmental regulations were stringent. Furthermore, they were supported by a profound economic and technological foundation, enhancing the effect of environmental regulation and making it more prominent.In model (4), the effect of population density on EWP presented a significant positive single-threshold characteristic. When the hierarchy level was below 0.221, the elasticity coefficient of population density on EWP was 0.445, and 26 cities, such as Yichang and Shangrao, were within this range. After crossing this threshold, the elasticity coefficient of population density on EWP rose to 0.930, and five cities, including Wuhan and Changsha, were within this range. Higher hierarchy level cities had the scale effect and agglomeration effect of population agglomeration. A higher population density was conducive to promoting the intensive use of resources and improving economic benefits and EWP. However, for lower hierarchy level cities, scale and level limited the scale effect of population agglomeration and comprehensive effect. Thus, the impact of population density on EWP was weaker than in high hierarchy level cities.Among the four models, most of the elasticity coefficients of the degree of openness, government economic influence, and urban construction density on EWP did not pass the significance level test. The elasticity coefficients of the degree of openness in models 1, 3, and 4 were all negative, showing an insignificant inhibitory effect. The reason for this is that China restricted the transfer of highly polluting resource-intensive industries to its domestic area. Some international capital has also turned its attention to Southeast Asia rather than China. This means that the MRYRUA was no longer a “pollution sanctuary” for international capital. Furthermore, China is currently actively building a large domestic market cycle, meaning that the influence of foreign capital will be reduced further. Therefore, openness failed to have a significant impact on EWP. The elasticity coefficients of government financial influence in models 1, 3, and 4 were all positive, but the elasticity coefficient was not significant. The construction of an ecological civilization is an essential indicator for evaluating the functions of local governments in China. Governments with stronger financial influence can invest more labor and funds in public utilities such as environmental protection, education, and medical care. It can promote environmental friendliness, enhance public welfare and people’s well-being, and improve EWP. The elasticity coefficient of urban construction intensity in models 3 and 4 was positive, and the elasticity coefficient in models 1 and 2 was negative. Nevertheless, the coefficient only passed the 10% significance level test in Model 4. The higher the urban land use intensity, the more effective the land use can be. However, at the same time, there will also be inefficient sprawling developments, resulting in a low degree of intensive utilization and various ecological and environmental problems. Under these two effects, the urban construction intensity of the MRYRUA failed to impact EWP significantly.Sustainable development is one of the most critical issues in the world. The SDGs cover natural resources, the environment, human well-being, and other aspects. It shows that EWP is an effective tool to measure the progress of the SDGs. The expansion of cities, the growth of industries, and the increase in population have brought many pressures on local resources and the ecological environment, which have greatly affected the region’s sustainable development. An urban agglomeration or megalopolis is a highly integrated cluster of spatially compact and economically highly integrated cities. The high degree of integration of cities makes urban agglomerations one of the world’s most important carriers of human production, life, and economic development . Paying Significant differences were found in EWP levels among the cities within the MRYRUA. First, the UH level was not entirely consistent with the EWP level for most cities. Some cities at the middle and high UH levels had lower levels of EWP, while others at the lower levels had higher levels of EWP. This finding is similar to that of Knight et al.  who founFrom the hierarchy perspective, large-scale megalopolises, urban agglomerations, or small-scale metropolitan areas all form a hierarchical system of cities on different scales. Cities with different UH levels have different scales and positions in the UH system and also have different functions and roles. On this basis, a hierarchical effect is formed, which acts on the process of transforming ecological resources and the environment into an increase in human well-being. On the one hand, the highest level city in terms of hierarchy in the urban agglomeration play a critical leading role in the region. Higher ecological welfare performance means a coordinated “economic–social–environmental” development model. This model has a guiding role for cities at other levels and has diffusion and spillover effects on other cities as well. The sub-core or satellite city at the next hierarchy level assumes the medium function and expands the spatial scope of the diffusion effect of the high hierarchy level city. At the same time, they also need to improve their own EWP. On the other hand, affected by the hierarchical effect, the effects of various influencing factors differ between high-hierarchy and low-hierarchy cities. For example, the impetus of technological progress to EWP is more significant in high-hierarchy cities but weaker in low-hierarchy cities. Environmental regulation presents U-shaped among high, middle, and low hierarchy cities. Being alone in improving EWP is difficult for cities in urban agglomeration areas. No one-size-fits-all, immutable path exists for improving EWP. The role of cities at all hierarchy levels needs to be considered. Implementing local policies and strengthening collaboration to improve the relationship between the economy, society, and the environment is necessary.Exploring the spatial differentiation law of the EWP of the MRYRUA and the heterogeneity of influencing factors from a hierarchical perspective is of great theoretical and practical significance. This research provides a new perspective on promoting the sustainable development of urban agglomerations. This study is beneficial to the rapidly developing urban agglomerations in China, and other developing countries, in playing an overall better role in the urban agglomeration hierarchy system. This study can also better solve the problem of incongruity between economic development, resource, environment, and human well-being improvement. The government can propose targeted and differentiated EWP improvement paths in the future according to different UH levels.This study still has some limitations in expressing residents’ true well-being. Owing to the limitation of data acquisition, this study did not consider subjective well-being indicators such as resident satisfaction when measuring human well-being. The number of core explanatory variables in regression analysis is limited, and more influencing factors can be added for more in-depth research in the future. The EWP research framework constructed in this study reflects the relationship between resource and environmental consumption, economic growth, and well-being improvement. However, it did not define the constraints of ecological boundaries. High EWP levels that break ecological boundaries, or cities with a shallow level of well-being are both unsustainable. A low level of well-being undermines the ideal level of human development. Excessive ecological input will cause severe damage to the ecological environment that is difficult to reverse. In the future, the upper safety limit of ecological consumption and the just lower limit of human well-being development guarantee should be considered. EWP must be explored within the safe and just spatial boundaries of human social development.To improve the EWP of the MRYRUA, to promote the construction of ecological civilization, and the high-quality development of the urban agglomeration, the following suggestions are put forward.(1)The investment in scientific and technological innovation should be strengthened, and the allocation of innovation resources should be optimized. The capacity building of scientific and technological absorption as well as re-innovation should be expanded, and the transformation rate of scientific and technological achievements should be improved. According to the hierarchy level, different scientific and technological innovation strategies should be formulated to promote the flow of scientific and technological innovation elements and regional innovation cooperation. Collaborative innovation policies should be developed between neighboring cities. The innovation chain between cities should be opened up and industry–university–research cooperation between cities should be strengthened.(2)The vital role of industrial structure optimization in promoting technological innovation and industrial upgrading should be given full play. The development of the industrial structure should be promoted in the direction of rationalization and advancement. The development of green industries should also be accelerated. The important role of green and low-carbon industries in promoting regional development and managing the ecological environment should be enhanced.(3)Environmental pollution remains the bottleneck restricting the improvement of EWP. Cities at all levels need to strengthen environmental regulation further. Considering EWP’s “efficiency” and “fairness” in the future is necessary. A multi-party collaborative governance model for the ecological environment should be built. In the pollution control process, the government, enterprises, the public, and other social parties should be guided to participate together.(4)The restrictions on the urban population size should be properly lifted to give full play to the scale effect and intensive effect of the population. Intensive use of urban land and sprawling development should also be avoided.(5)The government of middle and low hierarchy level cities should be encouraged to transition from pursuing the traditional GDP target to the goal of improving people’s comprehensive well-being. The public’s well-being in income, health care, education, and ecological environment should be improved. The problem of imbalance and insufficiency should be solved, people’s growing needs for a better life should be met, and people’s life satisfaction should be improved.Based on the Super-SBM model, this study measured the urban-scale EWP of the MRYRUA in 2020 and analyzed its spatial differentiation characteristics. The threshold regression model was used to empirically test the heterogeneity and stage of the influencing factors of EWP at different UH levels. The main conclusions obtained are as follows.(1)The EWP of the MRYRUA showed significant spatial differentiation, and the overall trend was highest in the southwest and lowest in the northeast. The “core–periphery” situation with Wuhan, Changsha, and Nanchang as the core was apparent, and the phenomenon of “central collapse” occurred at the junction of sub-urban agglomerations. The hierarchy level of the city was not consistent with the EWP level. The high hierarchy level central cities of the urban agglomeration had higher EWP levels. Significant differences in performance levels were found between cities. The overall spatial differentiation characteristics indicate that the MRYRUA had a soft green and coordinated development capability; as such, spatial polarization of the EWP appeared. Promoting interactive and coordinated development among the cities in the MRYRUA is urgently needed. Strengthening the diffusion and radiation-driving effect of high-hierarchy-level cities is necessary.(2)A non-single linear relationship was found between the influencing factors of EWP and EWP in the MRYRUA. The impact of technological progress, industrial structure, environmental regulation, and population density on the EWP of the MRYRUA all showed threshold characteristics. In different UH intervals, influencing factors have different effects on EWP.(3)For different UH levels, the influencing factors of the EWP are heterogeneous and staged. The impact of technological progress, industrial structure, and population density showed a single threshold characteristic. The impact of environmental regulation showed the characteristics of double thresholds. Technological progress presented two-way, single-threshold, and two-stage characteristics. The industrial structure presented a significant negative single-threshold dual-stage feature. Environmental regulation presented a very significant positive double-threshold three-stage feature. Population density exhibited a significant positive single-threshold two-stage feature.


The products of the enterprise are the logistics objects of the enterprise. Therefore, the company's products are the primary factor affecting logistics costs. The products of different enterprises may be different in terms of the type, nature, volume, quality, and physical and chemical properties of the products, which will have different impacts on the cost of logistics activities such as warehousing, transportation, and material handling of enterprises. More and more enterprises have taken the cost of logistics distribution as one of the important indicators affecting the development of enterprises; especially, cold chain logistics has been rapidly developed and valued in recent years, because the requirements of such products for delivery time, distribution efficiency, and distribution environment are very strict, whether the goods can be distributed in a standard and reasonable environment and whether the delivery vehicle can deliver the goods within the time specified by the customer have greatly affected the safety of frozen and refrigerated food. Therefore, this paper reduces the cost of the distributor through the optimization of the distribution path of cold chain logistics and makes the goods distributed can be delivered to the customer faster and more reasonably by establishing an integrated optimization platform, which is of great significance for how to reduce the cost of enterprises. Therefore, this paper starts from the function with the lowest distribution cost as the goal, comprehensively considers the specific characteristics of cold chain logistics, given the relevant constraints, uses the improved genetic algorithm to iterate on the given scheme, sends the improved new scheme to the simulation software for simulation operation, then sends the results obtained by the operation to the genetic algorithm for the next iteration, and repeats it in turn until the prespecified conditions can be terminated. Therefore, this paper summarizes some problems and development status in cold chain logistics and distribution routes by consulting relevant literature, optimizes the scheme by using VRP model combined with constraints, establishes a distribution system model, and finally verifies and analyzes to obtain a more reasonable and satisfactory solution. The innovation of this paper is that the research on the VRP problem is optimized through an improved genetic algorithm, certain improvements have been made in the coding method and the operation of selection, crossover, variation, etc., and the improved genetic algorithm can greatly reduce the number of program iterations. Then, we use the integrated optimization platform to import the solution into FlexSim for simulation, each simulation of the new solution will be transmitted to MATLAB through the Excel table for the next optimization iteration, and we repeat the above steps until the preset conditions are met after the termination. This would lead to a more realistic and satisfactory solution. Food safety issues have always been the focus of attention in various countries and regions in the world. In recent years, frozen and refrigerated foods have become more and more popular in the Chinese market, but at the same time, they have also brought some troubles in logistics management and food safety problems. Since the end of the last century, there have been several incidents of food safety problems around the world, among which the Sanlu milk powder incident has been boiling in China and has had a great adverse impact . TherefoIn 1959, scholars first proposed the problem of vehicle scheduling, and after several generations of efforts, many specific VRP models for certain problems were produced, such as CVRP, time window VRP, multiwarehouse VRP, multimodel VRP, and open VRP. Among them, CVRP is the most basic model of VRP, which has the longest research time and fewer constraints. Time window VRP is currently the most studied in China, because this kind of VRP is more practical. At present, the main research abroad is the dynamic demand VRP, that is, to take into account that the needs of customers change with time and assume that the possibility of change is very large, how to improve the original distribution plan to adapt to this time-changing distribution problem. Due to the continuous research of VRP problems, it has also led to the emergence of various advanced algorithms, and it is precisely because of these advanced algorithms that the solution of VRP problems has become easier and easier .On the basis of the optimization of the distribution route of cold chain logistics, this paper uses the improved genetic algorithm and combines the integration optimization platform to better obtain a more satisfactory solution to the problem; in order to better meet the actual situation, the research in this paper is of great significance.Genetic algorithms in the general sense cannot solve VRP problems well, for example, genetic algorithms using binary coding are more troublesome, so this paper uses an improved genetic algorithm with an additional tangent point to optimize the problemBecause the VRP problem is a random dynamic problem, this paper combines MATLAB with FlexSim to establish an integrated optimization platform to optimize cold chain logistics distribution.Through the study of the transportation vehicle route problem of the logistics distribution center, the mathematical model of the VRP problem of logistics distribution is established. The immunogenetic algorithm is implemented using MATLAB, which is applied to solve VRP problems. By comparing the experimental results of this algorithm and the genetic algorithm, it shows the superiority of this algorithm in solving the VRP problem of logistics and distribution:The specific study of this paper on the distribution path optimization of fresh cold chain logistics based on genetic algorithm is shown in The general genetic algorithm cannot solve the VRP problem well, so the improved genetic algorithm of the double tangent point can greatly reduce the number of program iterations in this paper, so as to get a more satisfactory solution better and fasterIn order to better obtain a realistic model solution, this paper establishes an optimizer model in MATLAB and a distribution system model in FlexSim, combining the two into an integrated optimization platform to optimize distributionThe theoretical significance of cold chain logistics distribution optimization is as follows:By establishing an integrated optimization platform, it can achieve automatic and effective operation of each program of the distribution route, obtain the evaluation of each program and feedback to the user, and finally realize the efficient operation of the distribution route optimizationThe efficiency and cost of distribution are particularly important for the cold chain, so the cost of enterprise distribution can be greatly reduced by establishing an integrated optimization platformThe practical significance of cold chain logistics distribution optimization is as follows:Cold chain logistics is a systematic engineering that must be in a prescribed environment throughout the transportation process and the final sales stage to ensure the quality and performance of goods. Due to some breakthroughs in refrigeration technology in some developed countries in the middle of the last century, resulting in cold chain logistics, some foreign developed countries were the first to start researching and managing cold chain logistics, so the management ideas of these countries for logistics, especially cold chain logistics, have been quite perfect. Of course, this is closely related to the importance attached by developed countries to the cold chain, such as Japan, Canada, the United States, and other developed countries. The circulation rate of the cold chain is very high; for example, the circulation rate of meat and poultry through the cold chain reaches 100%, and the circulation rate of vegetables and fruits through the cold chain is also above 95%. At present, it is quite common for foreign countries to apply high-tech technology to cold chain logistics management . It is pAt present, there are still the following problems in China's cold chain logistics: (1) the government does not pay enough attention to it, and it is necessary to increase investment in the cold chain. (2) The laws and regulations on the cold chain are not yet perfect, and legal constraints in this regard should be strengthened. (3) There is bad competition between industries, so it is necessary to improve the standard system of related industries. (4) The focus should be on cultivating third-party logistics enterprises. In China, as a large agricultural country, every year will produce a huge number of agricultural products and even consider the problem of export to other countries, but for now, China's protection measures for meat products, fruits, vegetables are quite inadequate. Many distribution vehicles freezing technology is not in line with the standard, so every year, there will be nearly 50% of agricultural products suffered losses, so the cold chain should be given enough attention .Cold chain logistics has three main characteristics, including very high requirements for delivery time and delivery efficiency, a wide variety of goods, and extremely harsh distribution conditions, as shown in Timeliness: because the products carried by cold chain logistics are not easy to store, and they are perishable. Therefore, cold chain logistics must complete the operation in a short period of time to ensure that the product does not have any problems.Complexity: compared with normal temperature logistics, cold chain logistics involves refrigeration technology, thermal insulation technology, temperature and humidity detection, information system and product change mechanism research, and other technologies, some products even involve the constraints of laws and regulations, and each product is required by its corresponding temperature and humidity and storage time. Once the chain is broken, it will cause the previous efforts to be wasted, so it greatly increases the complexity of cold chain logistics.High cost: the cost of cold chain logistics is much higher than that of ambient logistics. First of all, the cost of equipment is high, the cost of cold chain logistics center warehouses and cold chain vehicles is generally several times that of room temperature warehouses and vehicles, and because it involves food and other special facilities and equipment, it requires a lot of capital investment. Secondly, the operating cost of cold chain logistics is high, and the cold storage needs uninterrupted cooling to ensure that the temperature is in a constant state, resulting in the high cost of electricity in the cold storage. Refrigerated trucks also need to be constantly cooled to ensure that the temperature of the product is constant, which requires more fuel costs. The long recovery period of cold chain logistics capital is not affordable by ordinary enterprises.There are three main features: Based on the characteristics of cold chain logistics discussed above, it is inevitable to optimize the distribution path of cold chain logistics, and the genetic algorithm is one of the most effective methods when optimizing the model. Genetic algorithm (GA) is an optimization technique based on natural selection and biological genetic mechanisms that is abstracted from the process of biological evolution. Professor J. H. Holland of Michigan University in the United States and others were inspired by Darwin's evolutionary theory of survival of the fittest in their research on how to build machines that can learn and first proposed the concept of “genetic algorithm.” In the 1990s, genetic algorithms developed rapidly in machine learning, pattern recognition, and control, and then more and more scholars have improved it accordingly, resulting in many improved genetic algorithms with more perfect and diversified functions, and at the same time, genetic algorithms have begun to receive more and more attention. The basic principle of the genetic algorithm is based on the law of “survival of the fittest” in nature. The algorithm is first given a random feasible initial solution group, and then by selecting, crossing, mutating, and a series of operations of each scheme, more possible solutions are generated, and then the solution with smaller adaptation value is eliminated according to the size of the given adaptability function value, and finally a condition for iterative termination is set to finally find a global optimal solution, which is an intelligent optimization algorithm. A schematic diagram of its basic principles is The survival law of organisms in nature is survival of the fittest, and genetic algorithms borrow this idea and apply it to the study of VRP problems. Genetic algorithm (GA) was created by J. Holand of the United States, and then according to the characteristics of the actual problem to be solved, many scholars have made corresponding improvements, such as sequential selection genetic algorithm, immune genetic algorithm, appropriate function calibration of genetic algorithm, large variation genetic algorithm, adaptive genetic algorithm, and multivariant adaptive genetic algorithm, etc. The algorithm is a global parallel search method, through certain iterative steps to finally find a global better solution to the problem . The algAlthough the traditional genetic algorithm has these advantages described above, it has problems such as insufficient local search ability, precocious puberty, and contradiction between search efficiency and accuracy of the solution. It is still far from enough to better solve the VRP problem, so this paper makes some necessary improvements to the traditional genetic algorithm and then solves the problem through the corresponding examples, and the operation process of the genetic algorithm in this paper is Since the VRP problem is a discrete event dynamic system, the running time of the transport vehicle on each road section is a random variable, so it is impossible to solve such problems with a simple adaptability function when optimizing the actual vehicle scheduling. Instead, it is necessary to use logistics simulation software such as FlexSim to obtain the adaptability value of the system through modeling and simulation. Therefore, the optimization process requires the integration of multiple tools  to be achieved .In the optimization process, the genetic algorithm has to evolve multiple generations, the number of populations (number of schemes) in each generation is multiple, and the adaptability of each scheme must be simulated multiple times in FlexSim to obtain . This reThe above problems can only be better solved by establishing an integrated optimization platform. Previous research has rarely used the integration platform to optimize the VRP problem, this paper takes the actual delivery speed of the vehicle as a random variable to study the VRP problem containing the customer's fuzzy appointment window, so it is necessary to integrate FlexSim with MATLAB to establish an optimization platform. The VRP problem is optimized and studied, which is conducive to improving the efficiency of optimization. The platform also provides an easy tool for similar optimization work in the future .FlexSim is responsible for protocol evaluation, which converts the protocol parameters into FlexSim models and evaluates the suitability of the scheme through simulation.MATLAB is responsible for performing genetic algorithms that not only generate the initial generation but also sort, screen, and generate a new generation of protocols based on the suitability given by FlexSim.Excel is responsible for the data storage and exchange between FlexSim and MATLAB.The integrated optimization platform in this paper first uses an improved genetic algorithm to randomly generate a set of initial solution spaces in Matlab. The initial solution is generated by a touch button to generate about dozens of initial sequence groups and then use another touch button to generate a dynamic editable text box and then enter the original data represented by the initial sequence group; then, use a touch button to transfer the data from this editable dynamic text box to an Excel table and then FlexSim . The simThis paper first introduces the relevant domestic and foreign literature on cold chain logistics and VRP problems and points out the shortcomings and development trends of cold chain logistics development. Secondly, the relevant theoretical knowledge and algorithms of VRP problems are summarized, which lays a foundation for further research in the article. Then, the improved genetic algorithm is used to establish the corresponding optimizer model in MATLAB, the corresponding distribution system model in FlexSim, and finally by combining the two to establish an integrated optimization platform, the VRP problem is optimized and studied as a target function of cost minimization. Finally, the corresponding solution was obtained by experimenting on a specific study, and the experimental results were analyzed .Adaptive genetic algorithm (AGA) is an improvement of the traditional genetic algorithm, which greatly improves the evolutionary ability of the genetic algorithm and accelerates the convergence speed through the adaptive adjustment of genetic parameters. When the genetic algorithm solves the problem, the cross operator provides a search scheme with a large span and coarse granularity, which is conducive to improving the probability of searching to the global optimal solution, but the search performance of the local optimal solution is poor; the variation operator represents the genetic variation of the chromosome, which ensures the diversity of the population and the diversity of the chromosomes in the process of algorithm execution, but the performance in the late stage of population evolution is poor. This allows the introduction of AGA mutation strategies that can change the cross operator and the mutation operator. The specific processing flow of the proposed algorithm is shown in Pm and Pc is reduced, thereby being preserved as a good individual; when the fitness value of individuals within the population is less than the average, pc and Pm is increased, which increases the rate at which individuals in the constituent subpopulations are produced. In this way, in the later stage of the algorithm execution, when the population falls into the local optimal solution and the function values are relatively close, changing the values of Pm and Pc can effectively jump out of the local optimal solution, which is conducive to increasing the probability of searching for the global optimal solution. One way to improve it isThe AGA strategy refers to the fact that, in the process of population evolution, when the fitness value of individuals within the population is greater than the average, the value of Pc and Pm represent the crossover probability and the mutation probability respectively; P, P2, Pcmax and Pmmax, respectively, represent the values of the probability of crossover and the probability of variation before and after the change; f′, f′, fmaz, and favg indicate the fitness value, the maximum value of the fitness value, and the average value, respectively. Generally, take P2 = 0.002, Pml = 0.2, P = 0.5, and Pl = 0.8. In addition, taking into account that the optimal individual is not destroyed, formula  distribution in Hangzhou city, Zhejiang Province, was optimized, with 9 customers scattered in Xihu District and Jianggan District, and the distribution center in Yuhang District. Aquatic products are distributed by 3 vehicles, and each with a normal driving speed of 45 km/h, but since it is impossible for transport vehicles to operate at a speed at every moment during the actual delivery process, this article will be on FlexSim. The driving speed of the delivery vehicle is set to follow a certain uniform distribution, and the average can be averaged a certain number of times each time a scheme is run. The transportation cost is 2 yuan/km, and the load capacity is 2 t . Due to However, because the time requirements for each customer point are different, it must be tested in the delivery process.Taking into account the cost beyond the customer time window, the time requirements for each customer point are shown in As can be seen from the above figure, the time range of each customer point specific requirements for delivery is not the same, the actual demand of each customer point is also different, considering that the load capacity of the delivery vehicle is limited. It can be seen that the genetic algorithm in this paper avoids the convergence of algorithm precocious puberty and achieves smaller advantages. This is because the genetic algorithm adopts concentration-based selection update, which replaces the selection replication based on the fitness value of the general genetic algorithm, which effectively prevents the “precocious puberty” problem in the genetic algorithm and leads the search process to global optimality. So, for this article, each vehicle can complete the delivery of up to 4 customer points, so the actual delivery process needs to consider the above situations comprehensively. At this time, it is necessary to use FlexSim. The simulation software simulates these data and then comes up with a solution that is more realistic.This paper conducts a detailed and in-depth study of VRP and uses genetic algorithms to effectively solve VRP problems. By studying the combination of genetic algorithm and fresh cold chain transportation and using genetic algorithm to solve VRP, the following conclusions are obtained: genetic algorithm realizes the path optimization process of complex logistics distribution. It is more meaningful to study the temporary increase in orders from customers for the study of vehicle scheduling.Due to limited capacity, this paper did not conduct a study of this situation in the article, which is the shortcoming of the article and a direction to continue to study in the future; in addition, this paper only links the genetic algorithm and the FlexSim simulation software through the data in the excel table and does not realize how to quickly and effectively calculate the better distribution scheme. This is also an area that needs further study in the future.Secondly, this paper only gives a certain penalty cost to vehicles that exceed the customer time window, that is, a factor is given to control the additional penalty amount, and does not combine other aspects of logistics information to determine how to compensate vehicles that exceed the customer time window accordingly.Finally, since there are different kinds of uncertain information in the vehicle scheduling process, this paper only studies two related uncertain information in the vehicle scheduling process: the driving speed of the vehicle is uncertain, and the speed of vehicle loading and unloading is different. There are many uncertain factors about vehicle scheduling in real life, and at the same time, with the development of society, the factors affecting the logistics and distribution process have become more and more complex, and the vague information in it is more and more, and the fields involved are getting wider and wider.Looking forward to the future development of the logistics industry, in the context of the Internet era, relying on the discovery and analysis of big data will provide strong theoretical support for vehicle scheduling optimization, and the computer technology will be flexibly applied to logistics distribution, so as to make the logistics industry develop better and faster.