Currently, irrigation decisions in coffee cultivation primarily rely on empirical knowledge, resulting in inefficient practices. Combining real-time leaf water content (LWC) data can help improve the accuracy of the irrigation planning. Spectral remote sensing is a fast, reliable, and non-invasive method to detect vegetation moisture content. In this study, a model to estimate the LWC of Coffea arabica L. was built using hyperspectral reflectance of the canopy under various irrigation levels. For this purpose, common spectral indices, two-band spectral indices [ratio spectral index (RSI); difference spectral index (DSI); and normalized difference spectral index (NDSI)], and three-band spectral indices were constructed. Feature bands were extracted using the successive projections algorithm (SPA). Optimal spectral indices were extracted using the correlation coefficient method, and the feature wavebands and spectral indices were combined into five datasets. These datasets were split into modeling and validation datasets by sample set partitioning based on the joint x-y distance (SPXY) algorithm. A linear model [partial least squares regression (PLSR)] and three non-linear models [support vector machine (SVM); extreme learning machine (ELM); back propagation artificial neural network (BPANN)] were built to estimate LWC of Coffea arabica L. The results indicated that the non-linear models surpassed the linear model. The accuracy was the highest when the modeling was performed using the dataset combination 5. Among various modeling methods, the predictive performance of ELM was the best (modeling dataset: R2=0.745, RMSE=2.241%, RRMSE=3.482%; validation dataset: R2=0.721, RMSE=2.142%, RRMSE=3.364%). ELM outperformed PLSR, SVM, and BPANN in LWC retrieval. The obtained results indicated that the dataset built by the combined use of different methods was superior to those from a single data source in accuracy. This study provides a scientific basis for the quantitative diagnosis of coffee tree water status, with significant implications for optimizing field irrigation management.
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To explore the response of soil-plant system stoichiometry to elevational gradients during the dry and rainy seasons and to identify key factors influencing nutrient content variations, this study aimed to reveal nutrient cycling mechanisms and provide a scientific basis for the sustainable development of protected areas.
In Dry season and Rainy season of 2022, samples were collected from four elevation gradients (1 800, 2 100, 2 400, and 2 700 m) within the Liziping National Nature Reserve, followed by laboratory analysis.
The study investigated the stoichiometric characteristics of leaf carbon (C), nitrogen (N), and phosphorus (P) and their driving factors. The results showed that elevation, season, and their interaction significantly influenced the stoichiometric composition of plant leaves. Leaf C content exhibited a relatively narrow range of variation (366.61-445.16 g/kg in the dry season and 339.80-442.55 g/kg in the rainy season), remaining relatively stable between seasons, but showing a trend of initially increasing and then decreasing with elevation. Leaf N content increased at first and then declined with elevation during the dry season, while the opposite pattern occurred during the rainy season, with significant seasonal differences across elevations. Nitrogen limitation on plant growth was more pronounced, particularly at low elevations (1 800 m), where seasonal differences were minimal. Leaf P content was 2.56 g/kg in the dry season and 1.71 g/kg in the rainy season, both exceeding the global average for plant P content (1.49 g/kg). At mid-to-high altitudes (2 100 m, 2 400 m), the nitrogen (N) element levels are classified as weakly sensitive and weakly stable, respectively. For other altitudes, the stability of plant leaf indicators shows absolute stability. Across all elevations and seasons, leaf N: P ratios were below 14, suggesting that most plants were co-limited by N and P, with P limitation being more pronounced. Seasonal changes led to stabilization in plant growth and metabolic rates across elevations, reflecting strong adaptability. Correlation and redundancy analyses revealed that soil factors, including C, N, P, available phosphorus (AP), available nitrogen (AN), and the C: N ratio, were the primary drivers of leaf nutrient content, with nitrogen and soil water content (SWC) identified as the most critical factors.
Plant growth was predominantly limited by nitrogen availability, particularly in mid- to high-elevation regions, while phosphorus limitation was more evident at higher elevations. Under P-limited conditions, plants demonstrated strong homeostasis during growth. Variations in soil nutrient supply caused by seasonal and elevation regulated plant leaf nutrient content and stoichiometric characteristics.
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