Nitrogen and Rainfall Effects on Crop Growth—Experimental Results and Scenario Analyses

Nitrogen (N) fertilization is critical for crop growth; however, its effect on crop growth and evapotranspiration (ETc) behaviors under different amounts of rainfall is not well understood. As such, there is a need for studying the impact of nitrogen application rates and rainfall amounts on crop growth and ETc components. Agricultural system models help to fill this knowledge gap, e.g., the Root Zone Water Quality Model (RZWQM2), which integrates crop growth-related processes. The objective of this study is to investigate the effect of the nitrogen application rate on crop growth, soil water dynamics, and ETc behavior under different rainfall amounts by using experimental data and the RZWQM2. A field study was conducted from 2016 to 2019 with three nitrogen application rates (0, 70, and 130 kg N ha−1) for unirrigated winter wheat (Triticum aestivum L.), and two nitrogen application rates (0 and 205 kg N ha−1) for unirrigated corn (Zea mays L.). For the period of 1986–2019, the amounts of actual rainfall during each crop growth period are categorized into four groups. Each rainfall group is used as a rainfall scenario in the RZWQM2 to explore the interactions between the rainfall amounts and N levels on the resulting crop growth and water status. The results show that the model satisfactorily captures the interaction effects of nitrogen application rates and rainfall amounts on the daily ETc and soil water dynamics. The nitrogen application rate showed a noticeable impact on the behavior of soil water dynamics and ETc components. The 75% rainfall scenario yielded the highest nitrogen uptake for both crops. This scenario revealed the highest water consumption for wheat, while corn showed the highest water uptake for the 100% rainfall scenario. The interaction between a high nitrogen level and 50% rainfall yielded the highest water use efficiency, while low nitrogen and 125% rainfall yielded the highest nitrogen use efficiency. A zero nitrogen rate yielded the highest ETc and lowest soil water content among all treatments. Moreover, the impacts of the nitrogen application rate on ETc behavior, crop growth, and soil water dynamics differed depending on the received rainfall amount

A Method for Reflectance Index Wavelength Selection from Moisture-Controlled Soil and Crop Residue Samples

Reflectance indices are a method for reducing the dimensionality of spectral measurements used to quantify material properties. Choosing the optimal wavelengths for developing an index based on a given material and property of interest is made difficult by the large number of wavelengths typically available to choose from and the lack of homogeneity when remotely sensing agricultural materials. This study aimed to determine the feasibility of using a low-cost method for sensing the moisture content of background materials in traditional crop remote sensing. Moisture-controlled soil and wheat stalk residue samples were measured at varying heights using a reflectance probe connected to visible and near-infrared spectrometers. A program was written that used reflectance data to determine the optimal pair of narrowband wavelengths to calculate a normalized difference water index (NDWI). Wavelengths were selected to maximize the slope of the linear index function (i.e., sensitivity to moisture) and either maximize the coefficient of determination (R2) or minimize the root mean squared error (RMSE) of the index. Results showed that wavelengths centered near 1300 nm and 1500 nm, within the range of 400 to 1700 nm, produced the best index for individual samples. Probe height above samples and moisture content were examined for statistical significance using the selected wavelengths. The effect of moisture was significant for both bare soil and wheat stalks, but probe height was only significant for wheat stalk samples. The index, when applied to all samples, performed well for soil samples but poorly for wheat stalk samples. Index calculations from soil reflectance measurements were highly linear (R2 \u3e 0.95) and exhibited small variability between samples at a given moisture content, regardless of probe height. Index calculations from wheat stalk reflectance measurements were highly variable, which limited the usefulness of the index for this material. Based on these results, it is expected that crop residues, such as wheat stalks, will reduce the accuracy of remotely sensed soil surface moisture measurements

Discrimination of soil phases by dual energy x-ray tomography

Numerous soil ecological functions are influenced by soil structure through its impact on spatial and temporal distributions of soil particles, water, and air within the soil profile. The nondestructive technique of x‐ray computed tomography (CT) was used for studying soil structure. X‐ray attenuation determined for two energy levels (80 kV and 120 kV) was used to calculate distributions of water, air, and solids, as well as the voxel dry bulk density for two silt loam subsoils. The spatial resolution during scanning was 0.25 mm in the horizontal and 1 mm in the vertical direction. For different voxel sizes, the weighted mean of the derived volumetric water, air, and solid contents, and the dry bulk densities agreed with the sample's phase composition and dry bulk density obtained by weighing. The use of dual energy scanning to study the heterogeneity of soil structure and the spatial distribution of water, air, and solids is discussed

Classifying Reflectance Targets under Ambient Light Conditions using Passive Spectral Measurements

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two−step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%

Effect of Stoniness on the Hydraulic Properties of a Soil from an Evaporation Experiment Using the Wind and Inverse Estimation Methods

Stony soils are distributed all over the world. The study of their characteristics has gained importance lately due to their increasing use as agricultural soils. The effect that rock fragments exert on the soil hydraulic properties is difficult to measure in situ, and is usually derived from the fine earth properties. However, the corrections used so far do not seem accurate for all types of stony soils. Our objective was to assess the adequacy of estimating the hydraulic properties of a stony soil from the fine earth ones by correcting the latter by the volume occupied by rock fragments. To do that, we first assessed the validity of different approaches for estimating the hydraulic properties of a stone-free and a stony (40% rock fragments) cylinder prepared with samples from the same silt loam soil. The functions relating to the soil hydraulic properties (θ-h, K-h-θ) were estimated by the Wind method and by inverse estimation, using data from an evaporation experiment where the soil water content and pressure head were measured at different soil depths over time. Results from the evaporation experiment were compared to those obtained by applying the equation that corrects fine earth properties by the rock fragments volume. Wind and the Inverse Estimation methods were successfully applied to estimate soil water content and hydraulic conductivity from the stony soil experiment, except for some uncertainties caused by the limited range of suction in which the experiment was conducted. The application of an equation for adjusting the soil water content at different pressure heads (allowing for defining the soil water retention curve, SWRC), and the unsaturated hydraulic conductivity (K) directly from the stone content was not satisfactory. K values obtained from the measured data were higher than those inferred by the correcting equation in the wet range, but decreased much faster with a decreasing pressure head. The use of this equation did therefore not take into account the effect that the creation of lacunar pores by the presence of rock fragments likely exerts on water flow processes. The use of such correction needs therefore to be revised and new approaches are needed for estimating the hydraulic conductivity in stony soils. In relation to SWRC, a new equation to calculate the water content of a stony soil accounting for the influence of possible lacunar pores is proposed