Browsing by Author "Eickhoff, Elizabeth"

Now showing 1 - 5 of 5
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    Active optical sensor measurements and weather variables for predicting winter wheat yield
    (Agronomy Journal, 2021) Aula, Lawrence; Omara, Peter; Nambi, Eva; Oyebiyi, Fikayo B.; Dhillon, Jagmandeep; Eickhoff, Elizabeth; Carpenter, Jonathan; Raun, William R.
    Accurate winter wheat (Triticum aestivum L.) grain yield prediction is vital for improving N management decisions. Currently, most N optimization algorithms use in-season estimated yield (INSEY) as a sole variable for predicting grain yield potential (YP). Although evidence suggests that this works, the yield prediction accuracy could be further improved by including other predictors in the model. The objective of this work was to evaluate INSEY, pre-plant N rate, total rainfall, and average air temperature from September to December as predictors of winter wheat YP. An 8- yr (2012–2019) data set for grain yield was obtained from Experiment 502, Lahoma, OK. The experiment was designed as a randomized complete block with four replications and N applied at 0, 45, 67, 90, and 112 kg ha–1.Weather data was obtained from the OklahomaMesonet (http://mesonet.org). The data were analyzed using R statistical computing platform. The best model was selected using least absolute shrinkage and selection operator. Root mean square error (RMSE) was obtained using k-fold cross-validation. The model selection algorithm produced the full model as the best model for yield prediction with an R2 of .79 and RMSE of 0.54 Mg ha–1. The best one-variable model – as expected – used INSEY as the predictor and had the highest RMSE of 0.72 Mg ha–1 and an R2 of .62. Mid-season YP prediction accuracy could be improved by including pre-plant N rate, mean air temperature, and total rainfall from September to December in a model already containing INSEY.
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    Effect of winter wheat cultivar on grain yield trend under different nitrogen management
    (Agrosystems, Geosciences & Environment,, 2020) Aula, Lawrence; Omara, Peter; Eickhoff, Elizabeth; Oyebiyi, Fikayo; Dhillon, Jagmandeep S.; Raun, William R.
    In many developing countries, crop production is achieved with little or no application of fertilizer N. Understanding grain yield trends as new winter wheat cultivars (Triticum aestivum L.) are released and grown under different N management is important for crop yield improvement. This study evaluated grain yield trends of winter wheat cultivars over time in a crop production system with and without N application. Yield data was obtained from two long-term experiments; 502 (E502) and 222 (E222) between 1969 and 2018. Results showed a mean annual grain yield increase of 12 and 30 kg ha–1 yr–1 as new cultivars were released and grown under adequate N management in E222 and E502, respectively. However, without N application, yield declined annually by 2.4 kg ha–1 yr–1 in E222 and increased marginally by 0.6 kg ha–1 yr–1 in E502. Nonetheless, the yield increase or decrease was only significant for E502 at 112 kg N ha–1 (r2 = .145; p = .01) and its slope was significantly different from that of control treatment (p = .02). In both experiments, yield was significantly influenced by cultivar and N interaction (p < .01), an indication that yield changed according to the level of N applied. In general, when N was applied, grain yields were high as well. New cultivars released over time improved grain yield with adequate N management.
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    Improving winter wheat grain yield and nitrogen use efficiency using nitrogen application time and rate
    (Agrosystems, Geosciences & Environment, 2021) Aula, Lawrence; Omara, Peter; Oyebiyi, Fikayo B.; Eickhoff, Elizabeth; Carpenter, Jonathan; Nambi, Eva; Fornah, Alimamy; Raun, William R.
    Preplant nitrogen (N) application, which involves placing nutrients in the soil before seeding, has been an integral part of crop production systems for decades. Some producers are known to apply N at least 21 d before planting. This may increase N loss and lower grain yield. This study evaluated the effect of timing and rate of N application on winter wheat (Triticum aestivum L.) grain yield and N use efficiency (NUE). An experiment with a factorial arrangement of treatments was set up in a randomized complete block design with three replications. Treatments included four N rates (0, 45, 90, and 135 kg ha–1) with each applied 7 or 30 d before planting, and at Feekes 5 (FK5). Grain N was analyzed using LECO CN dry combustion analyzer. The difference method [Grain N from (fertilized plot – check plot)]/N applied was used to compute NUE. Nitrogen application rate significantly affected grain yield (P ≤ .01). Although the rate may be temporally and spatially variable, approximately 90 kg N ha–1 was required to obtain yields that differ markedly from the check. Midseason applied N (FK5) had similar yields to preplant applied N in two of four siteyears and significantly increased yield at one site in 2020. Out of two sites, the timing of N application had a substantial effect on NUE in both years (P ≤ 0.11). In this case, NUE was increased by as much as 9.5% for midseason applied N compared to 30 d before planting N application time.
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    Nitrogen management impact on winter wheat grain yield and estimated plant nitrogen loss
    (Agronomy Journal, 2020) Dhillon, Jagmandeep; Eickhoff, Elizabeth; Aula, Lawrence; Omara, Peter; Weymeyer, Gwen; Nambi, Eva; Oyebiyi, Fikayo; Carpenter, Tyler; Raun, William
    Method of N application in winter wheat (Triticum aestivum L.) and its impact on estimated plant N loss has not been extensively evaluated. The effects of the pre-plant N application method, topdress N application method, and their interactions on grain yield, grain protein concentration (GPC), nitrogen fertilizer recovery use efficiency (NFUE), and gaseous N loss was investigated. The trials were set up in an incomplete factorial within a randomized complete block design and replicated three times for 5 site-years. Data collection included normalized difference vegetation index (NDVI), grain yield, and forage and grainNconcentration. TheNDVI before and after 90 growing degree days (GDD) were correlated with final grain yield, grain N uptake, GPC, and NFUE. At Efaw location, NDVI after 90 GDDs accounted for 58% of variation in grain yield and 51% variation in grain N uptake. However, NDVI was found to be a poor indicator of both GPC and NFUE. Grain yield was not affected by the method and timing of N application at Efaw. Alternatively, at Perkins, topdress applications resulted in higher yields. The GPC and NFUE were improved with the topdress applications. Generally, topdress application enhanced GPC and NFUE without decreasing the final grain yield. The difference method used in calculating gaseous N loss did not always reveal similar results, and estimated plant N loss was variable by site-year, and depended on daily fluctuations in the environment.
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    Unpredictable Nature of Environment on Nitrogen Supply and Demand
    (Agronomy Journal, 2019) Raun, William R.; Dhillon, Jagmandeep; Aula, Lawrence; Eickhoff, Elizabeth; Weymeyer, Gwen; Figueirdeo, Bruno; Lynch, Tyler; Omara, Peter; Nambi, Eva; Oyebiyi, Fikayo; Fornah, Alimamy
    The second law of thermodynamics states that entropy or randomness in a given system will increase with time. This is shown in science, where more and more biological processes have been found to be independent. Contemporary work has delineated the independence of yield potential (YP0) and nitrogen (N) response in cereal crop production. Each year, residual N in the soil following crop harvest is different. Yield levels change radically from year to year, a product of an ever-changing and unpredictable/ random environment. The contribution of residual soil N for next years’ growing crop randomly influences N response or the response index (RI). Consistent with the second law of thermodynamics, where it is understood that entropy increases with time and is irreversible, biological systems are expected to become increasingly random with time. Consequently, a range of different biological parameters will influence YP0 and RI in an unrelated manner. The unpredictable nature that environment has on N demand, and the unpredictable nature that environment has on final grain yield, dictate the need for independent estimation of multiple random variables that will be used in mid-season biological algorithms of the future.