Core Ideas Positive effect of genotype and N on yield and protein in grains and meal. No effect of genotype and N on concentration of oil in grain. High oleic genotypes showed higher protein concentration of grains and by‐products. Nitrogen increased the protein /oil ratio of the grains. Nitrogen improved the quality of sunflower by‐products. Sunflower (Helianthus annuus L.) conventional (CONV) and high oleic (HO) genotypes differ in yield and quality. Nitrogen affects grain yield, quality, and by‐products protein concentration. The objective was to evaluate the effect of genotype and N on grain yield, oil (OG) and protein (PG) concentration in grain and in by‐products (PM). The effect of genotype was evaluated in Exp. 1 with 7 CONV and 7 HO hybrids, at two planting dates (PD early and late). The effect of N (Exp. 2) was evaluated in 10 locations (3 with CONV and 7 with HO), under six N rates (0, 30, 60, 90, 120, and 150 kg N ha−1). We determined yield, OG, PG and PM. For the early PD of E1, yield was higher in HO than CONV genotypes (3822 kg ha−1 vs. 3495 kg ha−1). In Exp. 2, N increased yield in 50% of the locations (HO: 586; CONV: 597 kg ha−1). In Exp. 1, genotype did not affect OG, but PG was higher in HO than in CONV ones (18.0 vs. 16.8%, respectively). In Exp. 2, N did not affect OG, but increased PG in both types of genotypes. Consequently, PG/OG ratio increased with N rates. The higher PG, was also reflected in higher PM (44.0% HO and 38.8% CONV, respectively). Increases of 2.5% points in PG resulted in increases of 5.6 in PM. Therefore, the application of N would allow obtaining high yields and PG without detrimental effects on OG, improving the quality of grains and by‐products.

No abstract

Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status.

No abstract