Browsing by Author "Dhillon, Jagmandeep"

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 Spacing, Planting Methods and Nitrogen on Maize Grain Yield
    (Communications in Soil Science and Plant Analysis, 2020) Fornah, Alimamy; Aula, Lawrence; Omara, Peter; Oyebiyi, Fikayo; Dhillon, Jagmandeep; Raun, William R.
    Maize (Zea mays L.) production in the developing countries takes place on marginal landscapes using indigenous planting methods that conflict with appropriate row spacing (RS) and plant to plant spacing (PPS). A study was conducted to determine the effect of different RS, variable plant densities and different planting methods on maize grain yield. This study was conducted for two years at three locations in Oklahoma including Lake Carl Blackwell (Port silt loam), Efaw (Ashport silty clay loam), and Perkins (Teller sandy loam-fine-loamy). Fourteen treatments were evaluated at each location in a randomized complete block design with three replications. Treatments included two RS (0.51 m, 0.76 m), three nitrogen (N) application rates (0, 60, 120 kg N ha−1), two PPS (0.15 m, 0.30 m) and two planting methods (Greenseeder hand planter; farmers practice). Results showed an increase in grain yield by 34% in 2017 and 44% in 2018 for the narrow RS of 0.51 m compared to the 0.76 m RS. This was likely due to increased plant population at the narrow RS. This study suggests that maize producers in developing countries could use narrow RS (0.51 m) with wide PPS (0.30 m) to increase grain yields.
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    In-Season Application of Nitrogen and Sulfur in Winter Wheat
    (Agrosystems, Geosciences & Environment,, 2019) Dhillon, Jagmandeep; Dhital, Sulochana; Lynch, Tyler; Figueiredo, Bruno; Omara, Peter; Raun, W. R.
    Decreased atmospheric S deposition in the past 20 yr has led to increased S fertilizer consumption in winter wheat (Triticum aestivum L.). Producers often apply S without any soil test information. Experiments were conducted at Lahoma, Lake Carl Blackwell, and Perkins, OK (2011–2013) to assess the effect of N and S applied preplant and foliar on grain yield and grain N for winter wheat. In 2011–2012, urea ammonium nitrate (UAN) was applied preplant at rates of 40 and 80 kg N ha-1 additionally; UAN and urea-triazone (NSURE) were foliar-applied at rates of 10 and 20 kg N ha-1. Sulfur was foliar-applied as gypsum (CaSO4×2H2O) at 6 kg S ha-1. In 2013, trials were altered to apply 40 kg N ha-1 as UAN preplant, and 20 kg N ha-1 foliar-applied. Gypsum rates were adjusted at 0, 3, and 6 kg S ha-1 preplant, and S (MAX-IN-S) at 3 and 6 kg S ha-1 was foliar-applied. Sulfur did not increase grain yield or grain N concentration at any site. The interaction between foliar S and N and preplant S and N was not significant. Sulfur fertilizer application is less likely to benefit this region unless low levels of soil test S are identified before planting. Use of recommended soiltesting guides are encouraged. Although S applications are encouraged commercially, no response was observed in these trials, and all were on sites where soil organic carbon was low (<8.5 g kg-1), where the possibility of seeing S deficiency was greater.
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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.