Table 2_Genome-wide association studies for identification of stripe rust resistance loci in diverse wheat genotypes.xlsx Dahkki Vikesh Tanwar (22809038)

“…Marker–trait associations were identified using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU approaches, considering loci with –log₁₀(p) ≥ 3 as significant.…”

Image 1_Genome-wide association studies for identification of stripe rust resistance loci in diverse wheat genotypes.jpeg Dahkki Vikesh Tanwar (22809038)

“…Marker–trait associations were identified using General Linear Model (GLM), Mixed Linear Model (MLM), and FarmCPU approaches, considering loci with –log₁₀(p) ≥ 3 as significant.…”

Image 1_Toxicological effects of sublethal microcystin-LR exposure in Labeo rohita: histopathological, ultrastructural, immunological, and biochemical impairments.jpeg Dahkki Snatashree Mohanty (22459801)

“…Interestingly, the modulation in the expression of SOD, catalase, GST, CYP1A and CYP3A genes in different organs indicated their involvement in the antioxidant and detoxification process. …”

Table 1_Strawberry-herb intercropping: a 2-year study toward sustainable intensification and diversification.docx Dahkki Sebastian Soppelsa (5776868)

“…Despite its ecological benefits, its adoption in specialized farming systems—such as strawberry monocultures—remains limited, as these systems typically focus on maximizing income from a single crop. …”

Table 8_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Table 6_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Image 2_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.png Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Table 7_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Table 3_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Table 5_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Table 1_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Table 2_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.docx Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Table 4_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.xlsx Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Image 1_Dissecting adult plant resistance to stem rust through multi-model GWAS in a diverse barley germplasm panel.png Dahkki Yuliya Genievskaya (4725192)

“…Phenotypic data were combined with high-density SNP genotyping to perform GWAS using five statistical models (GLM, MLM, MLMM, FarmCPU, and BLINK). Population structure and kinship were accounted for to identify robust marker-trait associations (MTAs), followed by haplotype-based QTL delineation. …”

Table 3_Age-stratified gut microbial changes in diarrheal calves: insights from 16S rRNA sequencing across early development.xlsx Dahkki Yanli Zhang (309699)

“…</p>Materials and methods<p>This study investigated 60 female Holstein calves (1, 21, and 30 days old) from a commercial dairy farm, equally divided between healthy and diarrheal groups based on standardized fecal scoring. …”

Table 1_Age-stratified gut microbial changes in diarrheal calves: insights from 16S rRNA sequencing across early development.xlsx Dahkki Yanli Zhang (309699)

“…</p>Materials and methods<p>This study investigated 60 female Holstein calves (1, 21, and 30 days old) from a commercial dairy farm, equally divided between healthy and diarrheal groups based on standardized fecal scoring. …”

Table 2_Age-stratified gut microbial changes in diarrheal calves: insights from 16S rRNA sequencing across early development.xlsx Dahkki Yanli Zhang (309699)

“…</p>Materials and methods<p>This study investigated 60 female Holstein calves (1, 21, and 30 days old) from a commercial dairy farm, equally divided between healthy and diarrheal groups based on standardized fecal scoring. …”

Assessing contract solutions for agricultural public goods in the Netherlands Dahkki Kina S. Harmanny (21629360)

“…<p>Farming significantly influences public goods (PGs) like water quality and biodiversity, both positively and negatively. …”

How land use right transfers improve fertilizer efficiency in China: a systematic review Dahkki Jiali Yang (2918099)

“…Land transfer's chemical fertilizer reduction effect is pronounced on food crops rather than cash crops. Farmers involved in the land transfer can expand livestock production while growing crops and boosting organic waste recycling. …”

UNIMELB_LAMBERT-Holly_VYT-LOCAL-2025.mp4 Dahkki Holly Lambert (20488115)

“…Antimicrobial resistance in the farm-to-plate continuum: more than a food safety issue. …”