Search Results (2)

Accurately detecting nitrogen (N) deficiency and determining the need for additional N fertilizer is a key challenge to achieving precise N management in many crops, including rice (Oryza sativa L.). Many remotely sensed vegetation indices (VIs) have shown promise in this regard; however, it is not well-known if VIs measured from different sensors can be used interchangeably. The objective of this study was to quantitatively test and compare the ability of VIs measured from an aerial and proximal sensor to predict the crop yield response to top-dress N fertilizer in rice. Nitrogen fertilizer response trials were established across two years (six site-years) throughout the Sacramento Valley rice-growing region of California. At panicle initiation (PI), unmanned aircraft system (UAS) Normalized Difference Red-Edge Index (NDRE_UAS) and GreenSeeker (GS) Normalized Difference Vegetation Index (NDVI_GS) were measured and expressed as a sufficiency index (SI) (VI of N treatment divided by VI of adjacent N-enriched area). Following reflectance measurements, each plot was split into subplots with and without top-dress N fertilizer. All metrics evaluated in this study indicated that both NDRE_UAS and NDVI_GS performed similarly with respect to predicting the rice yield response to top-dress N at PI. Utilizing SI measurements prior to top-dress N fertilizer application resulted in a 113% and 69% increase (for NDRE_UAS and NDVI_GS, respectively) in the precision of the rice yield response differentiation compared to the effect of applying top-dress N without SI information considered. When the SI measured via NDRE_UAS and NDVI_GS at PI was ≤0.97 and 0.96, top-dress N applications resulted in a significant (p < 0.05) increase in crop yield of 0.19 and 0.21 Mg ha⁻¹, respectively. These results indicate that both aerial NDRE_UAS and proximal NDVI_GS have the potential to accurately predict the rice yield response to PI top-dress N fertilizer in this system and could serve as the basis for developing a decision support tool for farmers that could potentially inform better N management and improve N use efficiency. Full article

Reflectance-based vegetation indices can be valuable for assessing crop nitrogen (N) status and predicting grain yield. While proximal sensors have been widely studied in agriculture, there is increasing interest in utilizing aerial sensors. Given that few studies have compared aerial and proximal sensors, the objective of this study was to quantitatively compare the sensitivity of aerially sensed Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-Edge Index (NDRE) and proximally sensed NDVI for assessing total N uptake at panicle initiation (PI-N_UP) and predicting grain yield in rice. Nitrogen response trials were established over a 3-year period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. At PI, a multispectral unmanned aircraft system (UAS) was used to measure NDVI_UAS and NDRE_UAS (average ground sampling distance: 3.7 cm pixel⁻¹), and a proximal GreenSeeker (GS) sensor was used to record NDVI_GS. To enable direct comparisons across the different indices on an equivalent numeric scale, each index was normalized by calculating the Sufficiency-Index (SI) relative to a non-N-limiting plot. Kernel density distributions indicated that NDVI_UAS had a narrower range of values that were poorly differentiated compared to NDVI_GS and NDRE_UAS. The critical PI-N_UP where yields did not increase with higher PI-N_UP averaged 109 kg N ha⁻¹ (±4 kg N ha⁻¹). The relationship between SI and PI-N_UP for the NDVI_UAS saturated lower than this critical PI-N_UP (96 kg N ha⁻¹), whereas NDVI_GS and NDRE_UAS saturated at 111 and 130 kg N ha⁻¹, respectively. This indicates that NDVI_UAS was less suitable for making N management decisions at this crop stage than NDVI_GS and NDRE_UAS. Linear mixed effects models were developed to evaluate how well each SI measured at PI was able to predict grain yield. The NDVI_UAS was least sensitive to variation in yields as reflected by having the highest slope (2.4 Mg ha⁻¹ per 0.1 SI). In contrast, the slopes for NDVI_GS and NDRE_UAS were 0.9 and 1.1 Mg ha⁻¹ per 0.1 SI, respectively, indicating greater sensitivity to yields. Altogether, these results indicate that the ability of vegetation indices to inform crop management decisions depends on the index and the measurement platform used. Both NDVI_GS and NDRE_UAS produced measurements sensitive enough to inform N fertilizer management in this system, whereas NDVI_UAS was more limited. Full article