# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 1 Introduction
Furthermore, four red-edge based indices--Red Edge Soil Adjusted Vegetation Index (RESAVI), Modified RESAVI (MRESAVI), Red Edge Difference Vegetation Index (REDVI), and Red Edge Re-normalized Difference Vegetation Index (REDVI)--showed similar effectiveness in estimating rice NNI across growth stages, with an R\({}^{2}\) of 0.76 [17]. These results highlight the importance of VIs in optimizing nitrogen management, reducing excessive nitrogen applications, and improving fertilizer efficiency. Phosphorus nutrition diagnostics remain less explored compared to nitrogen monitoring, with existing methods such as soil testing and tissue analysis presenting notable limitations. Soil testing, which offers insights into available phosphorus in the soil, is typically used for proactive fertility management before planting and is based on correlations between nutrient levels and crop yield responses [18]. However, it has significant drawbacks, primarily due to its reliance on a single snapshot of soil phosphorus status, which fails to capture the temporal variations in phosphorus availability throughout the growing season. Additionally, the accuracy of soil tests is influenced by the testing method and variability in soil sampling across large areas, resulting in inconsistencies. Similarly, while tissue analysis is crucial for evaluating phosphorus status during the growing season, it too has limitations. It can only be conducted when the crop is actively growing, and even if phosphorus deficiencies are detected, the effectiveness of in-season amendments remains uncertain because phosphorus is immobile in the soil, limiting plant access to added nutrients [19]. Moreover, interpreting phosphorus tissue concentrations is challenging as the critical and sufficiency ranges--representing nutrient concentrations below which yield responses occur and where added nutrients no longer increase yield but can still influence nutrient levels--vary by growth stage and plant part [20]. These complexities make it difficult to apply tissue analysis consistently across different growth stages and plant parts, limiting its practical use in large-scale phosphorus management for crops such as corn. Together, these challenges highlight the need for alternative, dynamic, and real-time phosphorus-monitoring systems. The combination of VIs and PNI offers a promising solution, enabling continuous, non-destructive monitoring of phosphorus status throughout the growing season. By integrating these indices, it becomes possible to capture the temporal fluctuations in phosphorus availability and better align fertilization practices with the crop's actual nutrient requirements, thus improving phosphorus use efficiency and minimizing environmental impact. This study investigates the potential of vegetation indices for estimating phosphorus nutritional status under varying phosphorus fertilizer levels. The objectives of the research are as follows: (i) to assess the relationship between vegetation indices and phosphorus nutritional status in maize under different fertilizer treatments and (ii) to construct a phosphorus absorption (Pabs) model for regional-scale phosphorus optimization and quantitative phosphorus nutrition diagnostics. The findings provide a theoretical framework for improving phosphorus management in maize, with practical implications for reducing fertilizer inputs and promoting sustainable agricultural practices.