Abatement of Nitrate Pollution in Groundwater and Surface Runoff from Cropland Using Legume Cover Crops with No-Till Corn

Agricultural practices can have a significant impact on water quality. The effects of leguminous winter cover crops on leaching of NO-3; from soil have been investigated in this project. Legume cover crops, by fixation of atmospheric N, can reduce the amount of fertilizer N required to produce summer grain crops. The methods initially used to evaluate cover crop effects on No; transport included suction probe lysimeters and measurement of NO-3; in soil samples collected to a depth of 90 cm. These measurements demonstrated extreme spatial variability in NO-3; distribution and water movement. This made it impractical to compare effects of different treatments. Soil transformations of legume and fertilizer N sources were compared using 15N labelled amendments. Less of the vetch N was found in leachable forms and, after 2 to 3 months in soil, losses of vetch N were smaller than losses of fertilizer N. To resolve the problem of spatial variability and to make direct measurements of leaching, 16 lysimeters were constructed from 55 gallon drums. These were treated with either fertilizer or legume N. Early measurements show greater NO-3; leaching with legume N, due to the mulch effect reducing evaporative water removal. However, there has been insufficient time to fully evaluate the treatments. This experiment will be continued

Associative Nitrogen Fixation Linked With Three Perennial Bioenergy Grasses In Field and Greenhouse Experiments

© 2020 The Authors. Associative nitrogen (N2)‐fixation (ANF) by bacteria in the root‐zone of perennial bioenergy grasses has the potential to replace or supplement N fertilizer and support sustainable production of biomass, but its application in marginal ecosystems requires further evaluation. In this study, we first combined both greenhouse and field experiments, to explore the N2 fixation effects of three temperate feedstocks Miscanthus × giganteus (giant miscanthus, Freedom), Panicum virgatum (switchgrass, Alamo), and Saccharum sp. (energycane, Ho 02‐147). In field studies across three growing seasons, plant and soil pools of candidate feedstocks were partially composed of N derived from the atmosphere (Ndfa). Energycane, giant miscanthus, and switchgrass were estimated to derive \u3e30%, %Ndfa. Greenhouse studies were also performed to trace isotopically labeled 15N2 into plant biomass and soil pools. Evidence for Ndfa was detected in all three feedstock grasses (using reference 15N of soil, chicory, and sorghum, δ15N~+7.0). Isotopically labeled 15N2 was traced into biomass (during grass elongation stage) and soil pools. Extrapolation of rates during the 24 hr labeling period to 50 days estimated 30%–55% of plant Ndfa, with the greatest Ndfa for energycane. The findings of the field natural abundance and greenhouse 15N2 feeding experiments provided complementary evidence that perennial bioenergy grasses have the potential to support relatively high rates of ANF, and accumulate diazotroph‐derived N into biomass when grown on non‐fertilized soil

Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1–V2 stage) and at the V6–V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs