Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

Background: Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Results: Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. Conclusions: With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing

Nitrogen Fertilizer Management in Dryland Wheat Cropping Systems

Wheat is the most widely cultivated food crop in the world, which provides nutrition to most of the world population and is well adapted to a wide range of environmental conditions. Timely and efficient rates of nitrogen (N) application are vital for increasing wheat grain yield and protein content, and maintaining environmental sustainability. The goal of this study was to investigate the effect of using different rates and split application of N on the performance of spring wheat in dryland cropping systems. The experiment was conducted in three different locations in Montana and Idaho during two consecutive growing seasons. A split-plot experimental design was used with three at planting N fertilization application (0, 90 and 135 kg N ha−1) and two topdressing N fertilization strategies as treatments. A number of variables such as grain yield (GY), protein content (GP) in the grains and N uptake (NUP) were assessed. There was a significant effect of climate, N rate, and time application on the wheat performance. The results showed that at-planting N fertilizer application of 90 kg N ha−1 has significantly increased GY, GP and NUP. On the other hand, for these site-years, increasing at-planting N fertilizer rate to 135 kg N ha−1 did not further enhance wheat GY, GP and NUP values. For all six site-years, topdress N fertilizer applied at flowering did not improve wheat GY, GP and NUP compared to at-planting fertilizer alone. As the risk of yield loss is minimal with split N application, from these results we concluded the best treatment for study is treatments that had received 90 kg N ha−1 split as 45 kg N ha−1 at planting and 45 kg N ha−1 at flowering

Evaluation of Sensor-Based Nitrogen Rates and Sources in Wheat

Nitrogen (N) is one of the most essential nutrients needed to reach maximum grain yield in all environments. Nitrogen fertilizers represent an important production cost, in both monetary and environmental terms. The aim of this study was to assess the effect of preplant nitrogen (N) rate and topdress N source on spring wheat (Triticum aestivum L.) grain yield and quality. Study was conducted in North-Central and Western Montana from 2011 to 2013 (total of 6 site-years). Six different preplant nitrogen (N) rates (0, 220, 22, 44, 67, and 90 N rate, kg ha−1) followed by two topdress N sources (urea, 46-0-0, and urea ammonium nitrate (UAN), 32-0-0) were applied to spring wheat (Triticum aestivum L.). The results showed that there were no significant differences in grain yield, protein content, or protein yield, associated with topdress N source

Effect of permeability on foam-model parameters: An integrated approach from core-flood experiments through to foam diversion calculations

We present a set of steady-state foam-flood experimental data for four sandstones with different permeabilities, ranging between 6 and 1900 mD, and with similar porosity. We derive permeability-dependent foam parameters with two modelling approaches, those of Boeije and Rossen (2015a) and a non-linear least-square minimization approach (Eftekhari et al., 2015). The two approaches can yield significantly different foam parameters. Thus, we critically assess their ability in deriving reliable foam parameter estimates. In particular, the way the two approaches treat shear-thinning foam behaviour and foam coalescence is discussed. The foam parameter set acquired from the latter approach is further used as input in foam diversion calculations: this serves to evaluate mobility predictions in non-communicating reservoir layers. This study aims to provide a framework to integrate experimental work, modelling and simple qualitative diversion calculations to provide a background for the upscaling of foam studies, with particular focus on heterogeneous systems

Effect of temperature on foam flow in Porous media

Foam can increase sweep efficiency within a porous medium, which is useful for oil-recovery processes (Farajzadeh et al., 2012). The flow of foam in porous media is a complex process that depends on properties like permeability, porosity and surface chemistry, but also temperature. Although the surface activity of surfactants as a function of temperature is well described at the liquid/liquid or liquid/gas interface, data on the effect of temperature on foam stability is limited, especially in porous media. In this work, we tested a surfactant (AOS) at different temperatures, from 20°C to 80°C, in a sandstone porous medium with co-injection of foam. The pressure drop, or equivalently the apparent viscosity, was measured in steady-state experiments. The core-flood experiments showed that the apparent viscosity of the foam can decrease by 50% when the temperature increased to 80°C. This effect correlates with the lower surface tension at higher temperatures. These results are compared to bulk foam experiments, which show that at elevated temperatures foam decays and coalesces faster. This effect, however, can be attributed to the faster drainage at high temperature, as a response to the reduction in liquid viscosity, and greater film permeability leading to faster coarsening. Our results show that one cannot fit foam-model parameters to data at one temperature and apply the model at other temperatures, even if one accounts for the change in fluid properties (surface tension and liquid viscosity) with temperature

Potential of Silicon Amendment for Improved Wheat Production

Many studies throughout the world have shown positive responses of various crops to silicon (Si) application in terms of plant health, nutrient uptake, yield, and quality. Although not considered an essential element for plant growth, Si has been recently recognized as a “beneficial substance” or “quasi-essential” due to its important role in plant nutrition, especially notable under stressed conditions. The goal of this study was to evaluate the effect of Si on wheat plant height, grain yield (GY), and grain protein content (GP). The experiment was conducted during two consecutive growing seasons in Idaho. A split-plot experimental design was used with three Si fertilization rates (140, 280, and 560 kg Si ha−1) corresponding to 100, 50, and 25% of manufacturer-recommended rates and two application times—at planting and tillering (Feekes 5). MontanaGrowTM (0-0-5) by MontanaGrow Inc. (Bonner, MT, USA) used in this study is a Si product sourced from a high-energy amorphous (non-crystalized) volcanic tuff. There was no significant effect of Si rate and application time on plant height, nutrient uptake, GY, or GP of irrigated winter wheat grown in non-stressed conditions. These results could be directly related to the Si fertilizer source used in the study. We are planning to further evaluate Si’s effect on growth and grain production of wheat grown in non-stressed vs. stressed conditions utilizing several different Si sources and application methods

Effect of temperature on foam flow in porous media

Foam can increase sweep efficiency within a porous medium, which is useful for oil-recovery processes [1]. The flow of foam in porous media is a complex process that depends on properties like permeability, porosity and surface chemistry, but also temperature. Although the surface activity of surfactants as a function of temperature is well described at the liquid/liquid or liquid/gas interface, data on the effect of temperature on foam stability is limited, especially in porous media.In this work, we tested a surfactant (AOS) at different temperatures, from 20 °C to 80 °C, in a sandstone porous medium with co-injection of foam. The pressure gradient, or equivalently the apparent viscosity, was measured in steady-state experiments. The core-flood experiments showed that the apparent viscosity of the foam decreased by 50% when the temperature increased to 80 °C. This effect correlates with the lower surface tension at higher temperatures. These results are compared to bulk foam experiments, which show that at elevated temperatures foam decays and coalesces faster. This effect, however, can be attributed to the faster drainage at high temperature, as a response to the reduction in liquid viscosity, and greater film permeability leading to faster coarsening.Our results using the STARS foam model show that one cannot fit foam-model parameters to data at one temperature and apply the model at other temperatures, even if one accounts for the change in fluid properties (surface tension and liquid viscosity) with temperature. Experiments show an increase in gas mobility in the low-quality foam regime with increasing temperature that is inversely proportional to the decrease in gas-water surface tension. In the high-quality regime, results suggest that the water saturation at which foam collapses fmdry increases and Pc* decreases with increasing temperature

UAV‐based NDVI estimation of sugarbeet yield and quality under varied nitrogen and water rates

Abstract 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