Browsing by Author "Nambi, Eva"

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    Spring wheat yield and grain quality response to nitrogen rate
    Walsh, Olga S.; Marshall, Juliet; Nambi, Eva; Shafian, Sanaz; Jayawardena, Dileepa; Jackson, Chad; Lamichhane, Ritika; Owusu Ansah, Emmanuella; McClintick-Chess, Jordan (Wiley, 2022-07-01)
    Nitrogen (N) is the most limiting nutrient in cereal production, yet its use efficiency remains very low at only 35%. Nutrient use efficiency (NUE) is crucial for increasing crop yield and quality while reducing fertilizer inputs and minimizing environmental damage. Optimum N rates that maximize yield without reducing NUE have been found to vary from location to location. This field study assessed the effect of N rates on the yield and quality of spring wheat (Triticum aestivum L.) at five locations in southern Idaho in 2015–2017. Nitrogen was applied as urea (46–0–0) immediately after planting at five rates: 0, 84, 168, 252, and 336 kg ha–1. Nitrogen application improved grain quality (increased protein) even when no increase in yield was noted. Nitrogen use efficiency and N uptake were affected by N rate at only 2 and 4 of 14 site-years, respectively. These observations highlight the challenging task of pinpointing the appropriate N rates for optimizing wheat yield, grain protein, N uptake and NUE; and the importance of adjusting N rates based on location, year, and prevalent environmental conditions.
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    UAV-based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates
    Walsh, Olga S.; Nambi, Eva; Shafian, Sanaz; Jayawardena, Dileepa M. M.; Ansah, Emmanuella Owusu; Lamichhane, Ritika; McClintick-Chess, Jordan R. R. (Wiley, 2023-03)
    The accuracy of the traditional soil and plant-based techniques for assessing sugarbeet demand for nitrogen (N) and yield prediction is generally low. Refining N and irrigation water management is a key to maximizing return for sugarbeet (Beta vulgaris L.) growers from agronomic, economic, and environmental perspective. The use of Normalized Difference Vegetative Index (NDVI) in combination with the unmanned aerial vehicle (UAV)-based data collection for in-season estimation of sugarbeet root yield and sugar concentration has potential for precision N management. Sugarbeet field trials were conducted in Idaho in 2019 and 2020 to assess (1) effects of water and N fertilizer rates on yield and estimated recoverable sugar (ERS) and (2) feasibility of predicting root yield and ERS using UAV NDVI. At the lowest N rate, application of water at 100% level resulted in greater yield, compared to 50%, in both years. At higher N rates, 50% level produced higher yields. At each N level, application of water at 100% level resulted in lower ERS, compared to 50%. The UAV NDVI was strongly correlated with root yield and ERS. The relationship between UAV NDVI and root yield and ERS was stronger in July (60 days after planting) compared to June (40 days after planting). Estimating the yield and ERS potential in late June/early July and topdressing the crop before the end of July may help to improve N use efficiency while optimizing sugarbeet production.
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    Wheat yield and protein estimation with handheld- and UAV-based reflectance measurements
    Walsh, Olga S.; Marshall, Juliet; Jackson, Chad; Nambi, Eva; Shafian, Sanaz; Jayawardena, Dileepa M.; Lamichhane, Ritika; Owusu Ansah, Emmanuella; McClintick-Chess, Jordan R. (Wiley, 2022-09-27)
    Precision agriculture provides efficient means of obtaining real-time data to guide nitrogen (N) management based on predicted crop profitability. This study was conducted to assess the efficacy of using in-season measurements (plant height, biomass weight, biomass N, soil plant analysis development [SPAD], GreenSeeker [GS] normalized difference vegetative index [NDVI], and unmanned aerial vehicle [UAV] NDVI) at Feekes 5 (tillering) and Feekes 10 (anthesis) to estimate wheat (Triticum aestivum L.) yield and protein. The secondary aim was to determine whether the accuracy of yield and protein prediction varies by wheat class and cultivar. Six cultivars—hard red spring (HRS) wheat ‘Jefferson’ and ‘SY Basalt’, hard white spring (HWS) wheat ‘Dayn’ and ‘UI Platinum’, and soft white spring (SWS) wheat ‘Seahawk’ and ‘UI Stone’—were planted at two locations in Idaho in 2018–2020. Plots were arranged in a randomized complete block design with four replications with each cultivar evaluated at seven N rates (0, 50, 100, 150, 200, 250, and 300 kg N ha–1). The determination of the Pearson correlation coefficients revealed that all parameters were linearly correlated with yield except for SPAD at Feekes 5 and biomass weight at Feekes 10. Although estimation of in-season grain protein remains a challenge, NDVI was strongly correlated with yield especially at Feekes 5. The accuracy of yield prediction was similar for all wheat classes. Comparable accuracy of yield estimation was achieved with GS NDVI and UAV NDVI. Both hand-held and aerial-based spectral measurements could be used to prescribe N rates to be applied during tiller formation when wheat yield can be optimized.