The use of genotype main effect (G) plus genotype‐by‐environment (GE) interaction (G+GE) biplot analysis by plant breeders and other agricultural researchers has increased dramatically during the past 5 yr for analyzing multi‐environment trial (MET) data. Recently, however, its legitimacy was questioned by a proponent of Additive Main Effect and Multiplicative Interaction (AMMI) analysis. The objectives of this review are: (i) to compare GGE biplot analysis and AMMI analysis on three aspects of genotype‐by‐environment data (GED) analysis, namely mega‐environment analysis, genotype evaluation, and test‐environment evaluation; (ii) to discuss whether G and GE should be combined or separated in these three aspects of GED analysis; and (iii) to discuss the role and importance of model diagnosis in biplot analysis of GED. Our main conclusions are: (i) both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega‐environment analysis and genotype evaluation, (ii) the GGE biplot is superior to the AMMI1 graph in mega‐environment analysis and genotype evaluation because it explains more G+GE and has the inner‐product property of the biplot, (iii) the discriminating power vs. representativeness view of the GGE biplot is effective in evaluating test environments, which is not possible in AMMI analysis, and (iv) model diagnosis for each dataset is useful, but accuracy gain from model diagnosis should not be overstated.

No abstract

No abstract

No abstract

No abstract

Nitrogen (N) fertilizer rates applied spatially according to crop requirements can improve the efficiency of N use. The study compares the performance of two commercial sensors, the Yara N-Sensor/FieldScan (Yara International ASA, Germany) and the GreenSeeker (NTech Industries Inc., Ukiah, California, USA), for assessing the status of N in spring wheat (Triticum aestivum L.) and corn (Zea mays L.). Four experiments were conducted at different locations in Quebec and Ontario, Canada. The normalized difference vegetation index (NDVI) was determined with the two sensors at specific growth stages. The NDVI values derived from Yara N-Sensor/FieldScan correlated with those from GreenSeeker, but only at the early growth stages, where the NDVI values varied from 0.2 to 0.6. Both sensors were capable of describing the N condition of the crop or variation in the stand, but each sensor had its own sensitivity characteristics. It follows that the algorithms developed with one sensor for variable-rate N application cannot be transferred directly to another sensor. The Yara N-Sensor/FieldScan views the crop at an oblique angle over the rows and detects more biomass per unit of soil surface compared to the GreenSeeker with its nadir (top-down) view of the crop. The Yara N-Sensor/FieldScan should be used before growth stage V5 for corn during the season if NDVI is used to derive crop N requirements. GreenSeeker performed well where NDVI values were [0.5. However, unlike GreenSeeker, the Yara N-Sensor/FieldScan can also record spectral information from wavebands other than red and near infrared, and more vegetation indices can be derived that might relate better to N status than NDVI.

No abstract

Developing efficient nutrient management regimes is a prerequisite for promoting canola (Brassica napus L.) as a viable cash crop in eastern Canada. Field experiments were conducted to investigate the growth, yield, and yield components of canola in response to various combinations of preplant and sidedress nitrogen (N) with soil-applied sulfur (S) and soil and foliar-applied boron (B). Canola yield and all its yield components were strongly correlated (r 2 = 0.99) with the amount of N applied, as was the above-ground biomass at 20% flowering and the leaf area index. Sidedress N was more efficiently utilized by the crop, leading to greater yields than preplant N application. On average, canola yields increased by 9.7 kg ha -1 for preplant N application and by 13.7 kg ha -1 for sidedress N application, for every kg N ha -1 applied, in 6 of the 10 site-years. Soil-applied S also increased canola yields by 3-31% in 7 of the 10 site-years, but had no effect on yield components. While there was no change in yield from soil-applied B, the foliar B application at early flowering increased yields up to 10%, indicating that canola plants absorb B efficiently through their leaves. In summary, canola yields were improved by fertilization with N (8 of 10 site-years), S (7 of 10 site-years) and B (4 of 10 site-years). Yield gains were also noted with split N-fertilizer application that involved sidedressing N between the rosette and early flowering stage. Following these fertilizer practices could improve the yield and quality of canola crop grown in rainfed humid regions similar to those in eastern Canada.