Modeling of P-Loss Risk and Nutrition for Mango (Mangifera indica L.) in Sandy Calcareous Soils: A 4-Years Field Trial for Sustainable P Management

The continuous addition of phosphorus (P) fertilizers above plant requirements increases P loss risks, especially if such fertilization practices continue long-term. The current study aims to determine the threshold value of P in plants and soil, which achieves the maximum mango fruit yield without P loss risk. P fertilizer doses (0–240 g tree−1) were added to 12-year-old mango (Mangifera indica L.) cv Hindy planted in sandy soil for four consecutive years. Soil and plant samples were collected each year to estimate the critical p values by linear–linear, quadratic, and exponential models. The relationships between fruit yield and available soil P were positive and significant in all the mathematical models. Mango fruit yield is expected to reach its maximum value if the sandy calcareous soil contains an available P amount ranging between 10–12 mg kg−1 and increasing the soil available P above this level leads to negligible increases in the fruit yield. Increasing the available soil P above 20.3 mg kg−1 increases P-loss risk. P concentrations in blades and petioles of mango leaves can be arranged as follows: beginning of the flowering stage > the full blooming stage > beginning of the fruiting stage. The analysis of petioles of mango leaves in the beginning of the flowering stage significantly corelated with mango fruit yield and can be used in predicting the response of mango to P fertilization. The findings of the present investigation revealed that the critical P in mango petioles ranged between 2.34 and 3.53 g kg−1. The threshold of available soil P for maximum fruit yield is half of P loss risks. The combined analysis of soil and plants is a powerful diagnostic tool for P management in sandy degraded soil. The findings of the current study are a good tool in achieving the optimum utilization of P fertilizer resources in maximizing mango fruit yield and reducing the risks of environmental pollution that result from excessive fertilization doses

Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress

Moisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the growth cycle. Proximal remote sensing can be a reliable technique for offering a rapid and precise instrument and localized management tool. This study tested the ability of proximal hyperspectral remotely sensed data to predict squash traits in two successive seasons (spring and fall) with varying moisture and potassium rates. Spectral data were collected from drip-irrigated squash that had been treated to varied rates of irrigation and potassium fertilization over both investigated seasons. To forecast potassium-use efficiency (KUE), chlorophyll meter (Chlm), water-use efficiency (WUE), and seed yield (SY) of squash, different commonly used and newly-introduced spectral index values for three bands (3D-SRIs), as well as a Decision Tree (DT) model, were evaluated. The results revealed that the newly constructed three-band SRIs based on the wavelengths of the visible (VIS), near-infrared (NIR), and red-edge regions were sensitive enough to measure the four tested parameters of squash in this study. For instance, NDI558,646,708 presented the highest R2 of 0.75 for KUE, NDI744,746,738 presented the highest R2 of 0.65 for Chlm, and NDI670,628,392 presented the highest R2 of 0.64 for SY of squash. The results further demonstrated that the principal component analysis (PCA) demonstrated the ability to distinguish moisture stress from potassium deficiency stress at the flowering stage onwards. Combining 3D-SRIs, DT-based bands (DT-b), and the aggregate of all spectral characteristics (ASF) with DT models would be an effective strategy for estimating four observed parameters with appropriate accuracy. For example, the model’s approximately 30 spectral characteristics were extremely important for predicting KUE. Its outputs with R2 were, for the training and validation datasets, 0.967 (RMSE = 0.175) and 0.818 (RMSE = 0.284), respectively. For measuring Chlm, the DT-DT-b-20 model demonstrated the best. In the training and validation datasets, the R2 value was 0.993 (RMSE = 0.522) and 0.692 (RMSE = 2.321), respectively. The overall outcomes showed that proximal-reflectance-sensing-based 3D-SRIs and DT models based on 3D-SRIs, DT-b, and ASF could be used to evaluate the four tested parameters of squash under different levels of irrigation regimes and potassium fertilizer

A novel chalcone compound as a reagent for the validation of pharmaceutical cefotaxime sodium preparations

This paper reports the synthesis of a new heterocyclic compound, 2,7-dicloropyrido[2,3-g]quinoline-3,8-dicarbaldehyde, from p-phenylene diamine by converting p-acetanilide (1) and reacting with Vilsmeier-Haack reagent (2). Chalcone(3) is produced when two moles of 2-acetyl naphthalene respond with the two carbaldehydes in compound (2). This chalcone serves as a reagent for quantifying amino group-containing pharmaceutical compounds. At the same time, a sensitive spectrophotometric technique has been created and used in pharmaceutical preparations to measure cefotaxime sodium by diazotizing and coupling with chalcone reagent. The recommended method diazotizes the medicinal molecule with sodium nitrite in an acidic solution to form the diazonium salt. This salt is combined with Chalcone reagent in an essential medium to create a stable and colorful azo dye soluble in water. Maximum absorption of the product is shown at 406 nm. Throughout the concentration range of 0.5–30 µg/ml, Beer's law was followed. The M absorptivity was (18381.44) liters mol-1. cm-1, the average recovery rate was 100.48 percent, and the relative standard deviation was less than 0.9 %. Injection-ready pharmaceutical preparations were the focus of the methodology