Use of UAV Imagery and Nutrient Analyses for Estimation of the Spatial and Temporal Contributions of Cattle Dung to Nutrient Cycling in Grazed Ecosystems

Nutrient inputs from cattle dung are crucial drivers of nutrient cycling processes in grazed ecosystems. These inputs are important both spatially and temporally and are affected by variables such as grazing strategy, water location, and the nutritional profile of forage being grazed. Past research has attempted to map dung deposition patterns in order to more accurately estimate nutrient input, but the large spatial extent of a typical pasture and the tedious nature of identifying and mapping individual dung pats has prohibited the development of a time- and cost-effective methodology. The first objective of this research was to develop and validate a new method for the detection and mapping of dung using an unmanned aerial vehicle (UAV) and multispectral imagery. The second objective was to quantify change over time in water-extractable organic carbon (WEOC), water-extractable phosphorus (WEP), and water-extractable nitrogen (WEN) in naturally-deposited dung that ranged from one to twenty-four days old. In addition, pre-analysis dung storage methods (refrigeration vs. freezing) were evaluated for their impact on laboratory analyses results. Multispectral images of pastures were classified using object-based image analysis. Post-classification accuracy assessment showed an overall accuracy of 82.6% and a Kappa coefficient of 0.71. Most classification errors were attributable to the misclassification of dung as vegetation, especially in spectrally heterogeneous areas such as trampled vegetation. Limitations to the implementation of this method for identifying and mapping cattle dung at large scales include the high degree of geospatial accuracy required for successful classification, and the need for additional method validation in diverse grassland environments. Dung WEN concentrations ranged from 1.20 g kg-1 at three days of age, to a low of 0.252 g kg-1 at 24 days. The highest WEOC values were in day-old dung, 19.25 g kg-1, and lowest in 14-day-old dung, 2.86 g kg-1. WEOC and WEN both followed exponential decay patterns of loss as dung aged. WEP was lowest at 1.28 g kg-1 (day one) and highest at 12 days (3.24 g kg-1), and dry matter and WEOC concentration were stronger determinants of WEP than age alone. Freezing consistently increased WEN and WEOC concentrations over fresh values, but WEP was inconsistent across ages in its response. This research provides new insight into dung nutrient dynamics and presents a novel method for studying them across large spatial and temporal scales. Advisors: Martha Mamo and Jerry Volesk

COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R2 = 0.90, NRMSE = 0.12), followed by RFR (R2 = 0.90 NRMSE = 0.15), and SVR (R2 = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage

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

Opportunities for Improving Livestock Production with e-Management Systems

There is increased interest in hardware and software that can support e-Management for grassland-based livestock industries. Managers of grazing livestock were early adopters of radio frequency identification (RFID) technologies automatically monitoring individual animal performance. Recent developments of remote sensing, automated individual recording and management, location based systems, improved data transfer and technologies that can be used in more extensive grazing systems are providing new opportunities for the development of e-Management systems. There is a need for better data integration and systems that can provide the best available information to enable better decision-making. For greater industry adoption of more integrated e-Management systems, there needs to be a clear economic value. With increased on farm monitoring and the expansion of digital data sources, grazing livestock production systems have the opportunity to expand production efficiency through the implementation of e-Management

Monitoring Spatial Heterogeneity of Pasture within Paddock Scale using a Small Unmanned Aerial Vehicle (sUAV)

Symposium mini revie

Rangeland Inventory and Monitoring With Unmanned Aerial System Imagery

Publicly managed rangelands today are seeing higher demand from society for the goods and services they can provide, including livestock production, wildlife habitat, myriad forms of recreation, and ecosystem services. Adaptively managed multiple-use lands could benefit from more objective and synoptic data to evaluate ecosystem function and to carry out and defend land health assessments that allow or exclude certain land use activities. Field methods to measure critical soil and vegetation indicators are well-established and becoming standardized across jurisdictions. However, field methods have two main limitations: 1) most can only observe small portions of the landscape, which may produce an incomplete picture of the status and trend of rangeland health; and 2) field methods cannot measure some indicators very well or not at all. This research focused on developing methods to measure soil and vegetation characteristics from unmanned aerial system (commonly known as drones) imagery, which can observe significantly more land than their field counterparts. I demonstrated the measurement of one soil (erosion/deposition) and four vegetation (forage utilization, fractional cover, vegetation height, canopy gaps) indicators using drone imagery and compared each with established field methods. The results show that drone imagery methods can serve as a complement to field methods or even a replacement in some cases. I found that drone imagery methods can precisely map topographic change and forage utilization across extents not previously possible. Imagery methods can outperform field methods for vegetation heights and canopy gaps in some vegetation communities. Drone-imagery indicators have matured to the point where they can start being integrated into adaptive land management. An online space dedicated to sharing imagery workflows amongst the range community could quicken the pace of identifying best practices to facilitate the transition toward this technology. Adopting drone-based inventory and monitoring data, however, will not replace field skills in plant identification, knowledge of vegetation phenology and succession, and logical interpretation of the data for land health assessments

Precision Agriculture for Crop and Livestock Farming—Brief Review

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.info:eu-repo/semantics/publishedVersio

The application of Earth Observation for mapping soil saturation and the extent and distribution of artificial drainage on Irish farms

Artificial drainage is required to make wet soils productive for farming. However, drainage may have unintended environmental consequences, for example, through increased nutrient loss to surface waters or increased flood risk. It can also have implications for greenhouse gas emissions. Accurate data on soil drainage properties could help mitigate the impact of these consequences. Unfortunately, few countries maintain detailed inventories of artificially-drained areas because of the costs involved in compiling such data. This is further confounded by often inadequate knowledge of drain location and function at farm level. Increasingly, Earth Observation (EO) data is being used map drained areas and detect buried drains. The current study is the first harmonised effort to map the location and extent of artificially-drained soils in Ireland using a suite of EO data and geocomputational techniques. To map artificially-drained areas, support vector machine (SVM) and random forest (RF) machine learning image classifications were implemented using Landsat 8 multispectral imagery and topographical data. The RF classifier achieved overall accuracy of 91% in a binary segmentation of artifically-drained and poorly-drained classes. Compared with an existing soil drainage map, the RF model indicated that ~44% of soils in the study area could be classed as “drained”. As well as spatial differences, temporal changes in drainage status where detected within a 3 hectare field, where drains installed in 2014 had an effect on grass production. Using the RF model, the area of this field identified as “drained” increased from a low of 25% in 2011 to 68% in 2016. Landsat 8 vegetation indices were also successfully applied to monitoring the recovery of pasture following extreme saturation (flooding). In conjunction with this, additional EO techniques using unmanned aerial systems (UAS) were tested to map overland flow and detect buried drains. A performance assessment of UAS structure-from-motion (SfM) photogrammetry and aerial LiDAR was undertaken for modelling surface runoff (and associated nutrient loss). Overland flow models were created using the SIMWE model in GRASS GIS. Results indicated no statistical difference between models at 1, 2 & 5 m spatial resolution (p< 0.0001). Grass height was identified as an important source of error. Thermal imagery from a UAS was used to identify the locations of artifically drained areas. Using morning and afternoon images to map thermal extrema, significant differences in the rate of heating were identified between drained and undrained locations. Locations of tiled and piped drains were identified with 59 and 64% accuracy within the study area. Together these methods could enable better management of field drainage on farms, identifying drained areas, as well as the need for maintenance or replacement. They can also assess whether treatments have worked as expected or whether the underlying saturation problems continues. Through the methods developed and described herein, better characterisation of drainage status at field level may be achievable