Soil texture is an important environmental factor that influences the crop productivity of wheat (Triticum aestivum) because it provides all the nutrients required for growth of the plants. The soil based on nutrients is classified into four classes: silt, clay, sand, and loam. Soil based on mineral particles was classified by the United States Department of Agriculture (USDA). According to USDA, loam particles have a size between sand (2.00–1.0 mm) and silt (0.05–0.002 mm), whereas clay is less than 0.002 mm. Analysis shows that the growth rate of Triticum aestivum in each soil sample is different. The sizes of seven plants were increased in loamy soil, to 47 cm, whereas in sandy soil, plants were 25 cm long. Seven plants were grown in clay soil, and had lengths of 28 cm. Finally, five plants were grown in silt soil, and reached a size of 38 cm. After fertilizing each plant in the different soils equally, that the productivity of plants in loamy soil was observed to be greater as compared to plants of other soil samples. Clay soil plants showed improvements as compared to sand and silt soil, although not as good as loam. The worst growing plants were observed in sandy soil. This shows that the growth of Triticum aestivum plants is better in loamy soil, and loamy soil is the most beneficial for wheat crop productivity.

One of the most used techniques for determining animal abundance is distance sampling. The distance sampling framework depends on the idea of a detection function, and a number of options have been suggested. In this paper, we provide a new flexible parametric model based on the Burr XII distribution. To be more specific, we use the survival function of the Burr XII distribution for novel purposes in this context. The proposed model is appealing because it meets all of the requirements for a reliable detection function model, such as being monotonically decreasing and having a shoulder at the origin. It also has the features of having various asymmetric properties and a heavy right tail, which are rare properties in this setting. In the first part, we provide its key characteristics, such as shapes and moments. Then, the inferential aspect of the model is investigated. The maximum likelihood estimation method is used to estimate the parameters in a data-fitting scenario. The estimates of population abundance are derived and compared with some existing parametric estimates. A simulation is run to assess how well the resulting estimates perform in comparison to other widely applied estimates from the literature. The model is then tested using two real-world data sets. Based on the famous goodness-of-fit statistics, we show that it is preferable to some of the well-established models.

Fenugreek (Trigonella foenum-graecum) is an aromatic plant that yields secondary metabolites, continuously used for the readiness of food and medicines. The current study is conducted to assess the impact of inoculation of mycorrhiza on the growth of Trigonella foenum-graecum in different soil samples. The current study presents two arrangements of treatment in experimental and control pots. Different soil samples were obtained from different areas for experimental and control pots. In experimental pots, mycorrhiza fungi inoculation is introduced. The six pots were filled with each soil sample. Out of the six pots, three were control pots, and three were inoculated pots. In each pot, 6 kg soil was filled. It was observed that there were more leaves in the experimental (inoculated) pots and fewer in the fenugreek (non-inoculated) pots. The fresh and dry weight of the shoots and roots was taken. However, it was examined that the fresh weight of the shoots and roots of the inoculated pots was more compared to the non-inoculated pots. But the difficulty is seen in the clay control pots of clay soil because of the compactness of the clay soil. It was concluded that fenugreek showed more growth in inoculated pots compared to non-inoculated pots. It was also concluded that mycorrhizal fungal showed symbiotic association with fenugreek plants.