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Estimating olive (Olea europaea L.) leaf area is an important aspect of monitoring plant health and evaluating growth processes in agriculture. Accurate estimation of leaf area allows for a better understanding of processes such as water and nutrient utilization, photosynthesis efficiency, respiration, and yield potential. This study aims to determine the most accurate, easy, and reliable leaf area estimation model using the geometric properties (length and width) of olive leaves. Additionally, the predictive performances of multiple linear regression (MLR) and artificial neural network (ANN) were compared. A total of 1320 leaf samples collected from 22 olive cultivars were used in the study. Leaf length and width were taken as input parameters, and both MLR and ANN models were developed for each cultivar. Both multiple linear regression (MLR) and artificial neural network (ANN) models demonstrated high predictive accuracy for olive leaf area estimation across 22 cultivars. The MLR models explained up to 96% of the variation in leaf area using leaf length (LL) and leaf width (LW), with low root mean square errors, indicating strong reliability. When cultivar identity was modeled as a categorical factor through dummy encoding, the model captured significant cultivar-specific effects without altering the overall predictive performance. The ANN models achieved slightly higher accuracy, with determination coefficients exceeding 0.99 and minimal prediction errors, confirming their superior ability to model nonlinear relationships. Across both approaches, leaf width contributed more strongly to leaf area than leaf length. Cultivar-specific differences were statistically significant for only a few genotypes, while most cultivars exhibited comparable patterns after adjustment for multiple testing. In conclusion, both MLR and ANN models demonstrated high accuracy in predicting olive leaf area, with ANN models showing slightly superior performance. However, MLR models also yielded highly reliable results, indicating that both approaches are viable for practical applications in olive cultivation. These predictive models can be effectively used for rapid, non-destructive phenotyping, growth monitoring, and precision management in olive breeding and production systems.

Background Pistachio ( Pistacia vera L.) is one of the most important nut crops in the world. There are about 11 wild species in the genus Pistacia, and they have importance as rootstock seed sources for cultivated P. vera and forest trees. Published information on the pistachio genome is limited. Therefore, a genome survey is necessary to obtain knowledge on the genome structure of pistachio by next generation sequencing. Simple sequence repeat (SSR) markers are useful tools for germplasm characterization, genetic diversity analysis, and genetic linkage mapping, and may help to elucidate genetic relationships among pistachio cultivars and species. Results To explore the genome structure of pistachio, a genome survey was performed using the Illumina platform at approximately 40× coverage depth in the P. vera cv. Siirt. The K-mer analysis indicated that pistachio has a genome that is about 600 Mb in size and is highly heterozygous. The assembly of 26.77 Gb Illumina data produced 27,069 scaffolds at N50 = 3.4 kb with a total of 513.5 Mb. A total of 59,280 SSR motifs were detected with a frequency of 8.67 kb. A total of 206 SSRs were used to characterize 24 P. vera cultivars and 20 wild Pistacia genotypes (four genotypes from each five wild Pistacia species) belonging to P. atlantica, P. integerrima, P. chinenesis, P. terebinthus, and P. lentiscus genotypes. Overall 135 SSR loci amplified in all 44 cultivars and genotypes, 41 were polymorphic in six Pistacia species. The novel SSR loci developed from cultivated pistachio were highly transferable to wild Pistacia species. Conclusions The results from a genome survey of pistachio suggest that the genome size of pistachio is about 600 Mb with a high heterozygosity rate. This information will help to design whole genome sequencing strategies for pistachio. The newly developed novel polymorphic SSRs in this study may help germplasm characterization, genetic diversity, and genetic linkage mapping studies in the genus Pistacia .

Pistachio is one of the most commercially important nut trees in the world. To characterize the genetic controls of horticultural traits and facilitate marker-assisted breeding in pistachio, we constructed an SSR-based linkage map using an interspecific F 1 population derived from a cross between the cultivar “Siirt” ( Pistacia vera L.) and the monoecious Pa-18 genotype of Pistacia atlantica Desf. This population was also used for the first QTL analysis in pistachio on leaf and shoot characters. In total, 1312 SSR primers were screened, and 388 loci were successfully integrated into parental linkage maps. The Siirt maternal map contained 306 markers, while the “Pa-18” paternal map included 285 markers along the 15 linkage groups. The Siirt map spanned 1410.4 cM, with an average marker distance of 4.6 cM; the Pa-18 map covered 1362.5 cM with an average marker distance of 4.8 cM. Phenotypic data were collected during the growing seasons of 2015 and 2016 for four traits: leaf length (LL), leaf width (LW), leaf length/leaf width ratio (LWR), number of leaflet pairs (NLL), and young shoot color (YSC). A total of 17 QTLs were identified in the parental maps. Four QTLs for LL and LW were located on LG2 and LG4, while four QTLs for LWR ratio on LG13 and LG14, two QTLs for NLL and two QTLs for YSC were on LG7 and LG9, respectively, with similar positions in both parental maps. The SSR markers, linkage maps, and QTLs reported here will provide a valuable resource for future molecular and genetic studies in pistachio.