regression model

Despite recent development in producing chemical medicines, associated side effects have led to increased use of medicinal plants and natural compounds. Soil salinity, especially in arid and semi-arid regions, is a serious threat to global agriculture. Nowadays, efforts have been made to find benchmarks that can effectively select salt-tolerant or salt-resistant genotypes. In this regard, the use of computer software to predict the indices can help us for screening the most tolerant ecotypes. The objectives of the present study were to determine the best indicators of salinity tolerance using intelligent and regression models for eighteen commercial ecotypes of mint. The seedlings were planted in plastic pots and arranged in a split factorial experiment in a randomized complete block design with four replicates. The treatments consisted of four levels of salinity (0, 2.5, 5 and 7.5 dS m-1), two levels of harvesting time, and 18 ecotypes. The plants were grown until the flowering stage and then harvested. There was a significant difference between ecotypes in terms of calculated indices at all three levels of salinity. Indicators such as TOL, MP, GMP, YSI, STI and HM showed a significant positive correlation with YS and YP at all three levels of salinity. The cluster analysis divided the ecotypes into three distinct groups based on the calculated indices at all levels of salinity. The principal component analysis revealed that the YP, YS, TOL, MP, GMP, YSI, STI and HM were more suitable among others salt stress indices. The sensitivity analysis at 2.5 dS m-1 salinity level showed that the HM, STI, YSI, YI, SSI and MP indices were of higher importance than the others. At 5 dS m-1 salinity level, the HM, STI, YSI, YI, GMP and MP indices showed the highest importance whereas at 7.5 dS m-1 salinity level, the STI, YSI, YI, GMP and YP indices indicated the highest importance. In general, the results suggest that ANN(MLP) model (R2 = 0.999) is the best model to predict at all salinity levels. E13, E14, E15, E16 and E18 ecotypes are the most salt tolerant ecotypes which can be used for the future breeding program.

Several methods have been proposed for recommendation of phosphorus fertilizers. Each of them only examines the concentration of phosphorus in the soil or plant, while none of them investigates the correlation between phosphorus concentrations in the soil and plant. In this study, a method called "integrated plant and soil system" (IPSS) is proposed to describe phosphorus fertilizer. In this system, for recommendation of phosphorus, the correlation between this element in soil and plant was used. For this purpose, 39 Washington Navel Orange orchards were selected in Jahrom region and from each orchard three trees were chosen. Samples were taken from soil and plants during two consecutive years and their phosphorus was measured. Orchards were divided into two categories, first group high-yield orchards and another includes all orchards. The correlation was run between soil properties and phosphorus of plant organs with the phosphorus of soil saturation extract samples. Factors were selected that shown significant correlation with the phosphorus of soil saturated extract, and multivariate regression was established between them. The results showed a significant correlation between phosphorus of plant organs and soil samples, and the highest correlation was observed between fruit phosphorus and phosphorus of soil saturation extract. Moreover, there was a significant correlation between phosphorus of plant organs, and the highest correlation was observed between fruit phosphorus with other plant organs. A equation was also obtained for each of the two orchard groups, these two equations can calculate the amount of phosphorus required for orange orchards.