Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Innovative approaches for measuring organism stress and behavioural integrity in flume facilities: Deliverable D8-IV

HYDRALAB+ aims to improve the usefulness and value of hydraulic laboratory facilities and is developing experimental guidelines that will allow researchers to successfully investigate complex scenarios representative of natural environments in a context of climate change. Within this framework it is often important to incorporate relevant biological elements in physical experiments, including the use of live vegetation. Notwithstanding efforts to maintain their health by careful husbandry, plants typically degenerate when introduced to flume settings. Physiological responses to degenerating health can affect their interactions with the flow so that experimental conditions are not representative of healthy specimens in situ. There is therefore a need to measure and evaluate the health of plants being used in hydraulic facilities, especially since behavioural integrity might be reduced before there are obvious signs of degeneration. Such measurements are not routinely made so there is a need to identify measurement techniques and methodological protocols for assessing vegetation health status in hydraulic laboratories. This deliverable identifies a technique established in plant physiology and horticulture for monitoring vegetation health status and shows how it can be applied in hydraulic laboratories with minimal impact on organisms. A simple and suitable test among those established in the relevant literature is validated by conducting experiments on freshwater macrophytes. From the relevant literature and the results of experiments reported herein, this deliverable provides an overview of the technique identified and establishes practical guidance on how to properly apply it in hydraulic experiments. The methodological protocol developed can potentially be integrated into established protocols used in ecohydraulics studies as a simple proxy of vegetation health status

Genetically–Mediated Leaf Chemistry in Invasive and Native Black Locust (Robinia pseudoacacia L.) Ecosystems

Black locust (Robinia pseudoacacia L.) is one of the few examples of an intra-continentally invasive species. Few genetic studies have been conducted on black locust and none compare North American invasive and native populations. Chapter 1 is a summary of what is currently known about the taxonomy of the species and the genetic structure of black locust populations. Because black locust is a nitrogen-fixing tree, it has the potential to greatly alter the ecosystems in which it invades. The goal of Chapter 2 is to characterize the genetic and chemical variation among populations throughout the native Appalachian region and in two invaded regions in the Northeast and Midwest regions of the U.S. Understanding the role that genetic identity contributes to altering ecosystem function may help elucidate how invasion can cause changes across local and regional scales. To assist in understanding the impact of black locust across ecosystems, it is essential to develop rapid and non-destructive means of estimating genetic and chemical characteristics. The focus of Chapter 3 is to test whether or not in situ leaf spectra-based models can be used to accurately determine leaf chemistries and predict genet membership

Uumanned Aerial Vehicle Data Analysis For High-throughput Plant Phenotyping

The continuing population is placing unprecedented demands on worldwide crop yield production and quality. Improving genomic selection for breeding process is one essential aspect for solving this dilemma. Benefitted from the advances in high-throughput genotyping, researchers already gained better understanding of genetic traits. However, given the comparatively lower efficiency in current phenotyping technique, the significance of phenotypic traits has still not fully exploited in genomic selection. Therefore, improving HTPP efficiency has become an urgent task for researchers. As one of the platforms utilized for collecting HTPP data, unmanned aerial vehicle (UAV) allows high quality data to be collected within short time and by less labor. There are currently many options for customized UAV system on market; however, data analysis efficiency is still one limitation for the fully implementation of HTPP. To this end, the focus of this program was data analysis of UAV acquired data. The specific objectives were two-fold, one was to investigate statistical correlations between UAV derived phenotypic traits and manually measured sorghum biomass, nitrogen and chlorophyll content. Another was to conduct variable selection on the phenotypic parameters calculated from UAV derived vegetation index (VI) and plant height maps, aiming to find out the principal parameters that contribute most in explaining winter wheat grain yield. Corresponding, two studies were carried out. Good correlations between UAV-derived VI/plant height and sorghum biomass/nitrogen/chlorophyll in the first study suggested that UAV-based HTPP has great potential in facilitating genetic improvement. For the second study, variable selection results from the single-year data showed that plant height related parameters, especially from later season, contributed more in explaining grain yield. Advisor: Yeyin Sh

Enhancing Farm-Level Decision Making through Innovation

New information and knowledge are important aspects of innovation in modern farming systems. There is currently an abundance of digital and data-driven solutions that can potentially transform our food systems. At a time when the general public has concerns about how food is produced and the impact of farm production systems on the environment, strategies to increase public acceptance and the sustainability of food production are required more than ever. New tools and technology can provide timely insights into aspects such as nutrient profiles, the tracking of animal or plant wellbeing, and land-use options to enhance inputs and outputs associated with the farm business. Such solutions have the ultimate aim of enhancing production efficiency and contributing to the process of learning about the advantages of the innovation, while ensuring more sustainable food supplies. At the farm level, any new information needs to be in a useful format and beneficial for management and farm decision-making. The papers in this Special Issue evaluate agri-business innovation that can enhance farm-level decision-making

Integration of Remote Sensing Approaches for In-Season Nitrogen Management

Nitrogen (N) management is being conducted at flat rate in Louisiana due to practicality and convenience, but the price of N fertilizer and high breakeven costs are forcing producers to find ways to reduce costs and optimize N application. In this scenario, precision agriculture technologies, specifically the use of optical sensors on board of unmanned aerial systems (UAS) to variable rate N application on farm is showing a promising approach to save inputs and reduce environmental impacts. However, the general goal of this research was to develop and evaluate in-season N management approaches for N recommendation in corn (Zea mays L.) fields using plant canopy sensors and UAS. The specific objectives were to: 1) investigate the differences in spectral reflectance bands and vegetation indices for sensing the N status of corn, through different hours of the day, under different weather conditions and sun irradiation angulation; and 2) evaluate an in-season N fertilizer recommendation algorithm based on an approach that reflects local conditions and needs for N fertilization using active crop canopy sensor and unmanned aircraft systems coupled with multispectral camera, and to validate and compare the algorithm proposed with other approaches. The experiments were conducted in three fields at the LSU Doyle Chambers Central Research Station located at Ben Hur Road, Baton Rouge, LA, 30.365°N, -91.166°W, with continuous corn during the growing seasons from 2018 to 2021. To investigate time of the day effects on active and passive sensor systems the experiment was conducted at the same location in corn during a day with percentages of cloudy coverage conditions varying from 80 to 100 %, with very few moments of cloud dispersion resulting in 100% of clear sky at the target area. The conclusion in this experiment addressing objective 1 is that the data obtained from passive sensors (commercial UAS camera and spectroradiometer), contrarily to the active crop canopy sensor, presented prominent significant variations in measurements at different times of the day, especially observed when ambient conditions changed solar radiation. This indicates higher sensitivity to changes during the day for the wavebands and vegetation indices derived using these sensors. For objective 2, the main conclusions are: (i) a practical and easy to implement algorithm approach was proposed and validated considering local conditions and implemented in-season, (ii) the use of the Chlorophyl Red Edge Vegetation Index (CIRE) obtained from the crop canopy reflectance with the approaches developed from local data to manage N status, can address spatial variability presented in fields through the different responses obtained for N fertilization across the sites analyzed, and (iii) the virtual approaches using both active and passive sensors, indicated relatively better performances based on yield and partial factor productivity (PFP) responses. Due to the easy implementation this finding suggests that this approach has great potential to be applied for N recommendations regardless of the type of sensor used to collect data

Production of the subtropical seagrass, Halodule wrightii Aschers., in lower Laguna Madre, Texas

The autecology of shoal grass, Halodule wrightii Aschers., was studied at 1.2 m depth from June 1995 to February 1997 in Lower Laguna Madre (LLM), Texas. Halodule wrightii in LLM received about 47% surface irradiance, but otherwise displayed lower growth rates and biomass in nutrient-poor rhizosphere and water-column environments compared to H. wrightii populations in other Texas estuaries. High tissue N content and low C:N ratios belied low growth dynamics. Halodule wrightii in LLM is probably nutrient limited. A high nutrient demand by H. wrightii in a nutrient-poor environment may explain, in part, its gradual displacement by Thalassia testudinum and Syringodium filiforme in LLM

Improving the remote estimation of soil organic carbon in complex ecosystems with Sentinel‑2 and GIS using Gaussian processes regression

Background and aims The quantitative retrieval of soil organic carbon (SOC) storage, particularly for soils with a large potential for carbon sequestration, is of global interest due to its link with the carbon cycle and the mitigation of climate change. However, complex ecosystems with good soil qualities for SOC storage are poorly studied. Methods The interrelation between SOC and various vegetation remote sensing drivers is understood to demonstrate the link between the carbon stored in the vegetation layer and SOC of the top soil layers. Based on the mapping of SOC in two horizons (0-30 cm and 30-60 cm) we predict SOC with high accuracy in the complex and mountainous heterogeneous páramo system in Ecuador. A large SOC database (in weight % and in Mg/ha) of 493 and 494 SOC sampling data points from 0-30 cm and 30-60 cm soil profiles, respectively, were used to calibrate GPR models using Sentinel-2 and GIS predictors (i.e., Temperature, Elevation, Soil Taxonomy, Geological Unit, Slope Length and Steepness (LS Factor), Orientation and Precipitation). Results In the 0-30 cm soil profile, the models achieved a R2 of 0.85 (SOC%) and a R2 of 0.79 (SOC Mg/ha). In the 30-60 cm soil profile, models achieved a R2 of 0.86 (SOC%), and a R2 of 0.79 (SOC Mg/ha). Conclusions The used Sentinel-2 variables (FVC, CWC, LCC/Cab, band 5 (705 nm) and SeLI index) were able to improve the estimation accuracy between 3-21% compared to previous results of the same study area. CWC emerged as the most relevant biophysical variable for SOC prediction

Improving Dual-Purpose Winter Wheat in the Southern Great Plains of the United States

This chapter covers the production and breeding status of winter wheat (Triticum aestivum L.) used for early-season animal grazing and late-season grain production in the Southern Great Plains of the United States. Besides, in the chapter, the current production status and needs, the drawbacks of current cultivars, breeding strategies of the crop, novel genomics tools, and sensor technologies that can be used to improve dual-purpose winter wheat cultivars were presented. We will focus on traits that are, in general, not required by cultivars used for grain-only production but are critical for cool-season forage production