Using proximal sensing parameters linked to the photosynthetic capacity to assess the nutritional status and yield potential in quinoa
Proximal sensing has been used extensively for decades to assess crop nitrogen (N) status using either a hand-held chlorophyll meter or vegetation indices such as the normalized difference vegetation index (NDVI) for various crops. However, little has been done on quinoa (Chenopodium quinoa Willd.). In this study, we investigated how the SPAD chlorophyll meter values and NDVI could be used as indicators for N status and how they can be linked to quinoa performance in terms of photosynthesis and yield. The objectives of this study were to: 1) evaluate SPAD values and NDVI as indicators of N status, 2) assess their relevance over the crop cycle, and 3) investigate their link to the performance in terms of net CO2 assimilation and grain yield at harvest. A pot experiment based on varying nitrogen and phosphorus (P) input conditions was conducted in the glasshouse at Cranfield University, UK. The results showed that both SPAD and NDVI correlated similarly with the leaf N content (%) (R2=0.76, R2=0.82, p<0.001, respectively). High correlations between SPAD and NDVI were also observed at 58 DAS (R2=0.67) and across the entire crop cycle (R2=0.84), validating the utility of both parameters for N status monitoring. Furthermore, significant differences between treatments were observed at different growth stages when SPAD and NDVI were measured across the crop cycle. Strong significant correlations between SPAD and NDVI with the net CO2 assimilation (Anet) (R2=0.86, R2=0.81, p<0.001, respectively) were recorded. SPAD values and NDVI significantly correlated with grain yield at harvest (R2=0.68, R2=0.80, p<0.001, respectively). While SPAD and NDVI are potentially useful tools to improve N fertilizer management and develop in-season yield predictions in quinoa at relatively low-cost, alternative non-saturating spectral indices need to be explored to improve accuracy.
| Item Type | Article |
|---|---|
| Open Access | Gold |
| Keywords | Chenopodium quinoa, SPAD, NDVI, N status, net CO2 assimilation, photosynthesis |
| Project | Designing Future Wheat - WP1 - Increased efficiency and sustainability, Image analysis for plant phenotyping - machine learning based methods for analysis of multi-model and multi-dimensional remote sensing data from high-throughput plant phenotyping, Phenotyping nutritional status of crops using remote sensing technologies |
| Date Deposited | 05 Dec 2025 10:36 |
| Last Modified | 19 Dec 2025 14:56 |


