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Agronomy
Special Issues
Remote Sensing Application for Monitoring Grassland and Forage Production
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Remote Sensing Application for Monitoring Grassland and Forage Production

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Grassland and Pasture Science".

Deadline for manuscript submissions: closed (20 July 2019) | Viewed by 22376

Special Issue Editors

Prof. Dr. Michael Wachendorf

E-Mail Website
Guest Editor
Section of Grassland Science and Renewable Plant Resources, Universität Kassel, Steinstraße 19, 37213 Witzenhausen, Germany
Interests: crop production; grassland ecosystems; remote sensing; bioenergy
Special Issues, Collections and Topics in MDPI journals
Dr. Thomas Möckel

E-Mail Website
Guest Editor
Universität Kassel - Grassland Science and Renewable Plant Resources, Steinstrasse 19, 37213 Witzenhausen, Germany
Interests: Sensor fusion, Grassland ecology, Agronomy, Hyperspectral drone images, Point cloud analysis

Special Issue Information

Dear Colleagues,

Rapidly growing population and a dwindling resource base requiring a sustainable development of available agricultural resources. Decision-makers need to monitor and analyse the changing resource bases of grassland ecosystem, to make informed decisions. Modern information technologies, such as Remote Sensing (RS) and Geographic Information System (GIS) technologies, can facilitate efforts in this direction. RS and GIS are being increasingly used as tools to assist in grassland resource inventory and integration of data and as a mechanism for analysis, modelling, and forecasting to support decision making. However, the small-scale botanical and structural heterogeneity in many grassland systems present challenges for sensor applications in grassland and forage production. These grassland characteristics create uncertainty in remote sensing-based evaluation of forage production systems.

This Special Issue calls for innovative methods and applications to improve grassland and forage production at different scales. The range of topics includes, but is not limited to:

  • Fusion of complementary sensor system (e.g. spectral and ultrasonic sensor combination)
  • Scaling issues in forage production (submeter - field - landscape)
  • Temporal variability of grassland parameter within-season and between years
  • Monitoring grassland biomass, nutrient value and biodiversity
  • Remote sensing informed management measures

Prof. Dr. Michael Wachendorf
Dr. Thomas Möckel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Grassland quality (e.g. nutrient status)
  • Grassland quantity (e.g. yield)
  • Hyperspectral remote sensing
  • LiDAR/RADAR
  • Sensor fusion
  • UAV based imagery collection
  • Seasonality

Published Papers (5 papers)

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Research

9 pages, 2526 KiB  
Article
Seed Yield and Lodging Assessment in Red Fescue (Festuca rubra L.) Sprayed with Trinexapac-Ethyl
by Zahra Bitarafan, Jesper Rasmussen, Jesper Cairo Westergaard and Christian Andreasen
Agronomy 2019, 9(10), 617; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy9100617 - 07 Oct 2019
Cited by 6 | Viewed by 3372
Abstract
Red fescue (Festuca rubra) is used in seed mixtures for lawns and pastures. It is prone to lodge at flowering, and plant growth regulators (PGRs) are used to prevent lodging, ensuring sufficient pollination. Seed yield and lodging were studied over three [...] Read more.
Red fescue (Festuca rubra) is used in seed mixtures for lawns and pastures. It is prone to lodge at flowering, and plant growth regulators (PGRs) are used to prevent lodging, ensuring sufficient pollination. Seed yield and lodging were studied over three years in a red fescue field established with four seeding rates (2, 4, 6 and 8 kg ha−1) and sprayed each year with three doses of the PGR trinexapac-ethyl (250 g L−1) (0, 0.3, 0.6 and 1.2 L ha−1). Half of each plot was sprayed with the PGR and the other half was left unsprayed as control. The degree of lodging was assessed by analysing drone images in the second year of the experiment and using a 10-point scale for scoring lodging at the ground. Generally, application of PGR increased the seed yield but the effect varied between years. There was no interaction between the PGR dosage and seeding rate. We found a positive correlation between the blue intensity of the images and lodging. PGR dosage significantly affected lodging evaluated by visual ranking and the blue intensity of the images, while the seeding rates did not affect lodging. Lodging affected seed yield negatively. Full article
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland and Forage Production)
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11 pages, 2172 KiB  
Article
An Object-Based Image Analysis Approach to Assess Persistence of Perennial Ryegrass (Lolium perenne L.) in Pasture Breeding
by Chinthaka Jayasinghe, Pieter Badenhorst, Junping Wang, Joe Jacobs, German Spangenberg and Kevin Smith
Agronomy 2019, 9(9), 501; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy9090501 - 30 Aug 2019
Cited by 6 | Viewed by 3508
Abstract
Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for [...] Read more.
Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for livestock producers to use their agricultural lands more productively and intensively within sustainable limits. Breeding perennial ryegrass cultivars that are both productive and persistent is a target of forage breeding programs and will allow farmers to select appropriate cultivars to deliver the highest profitability over the lifetime of a sward. Conventional methods for the estimation of pasture persistence depend on manual ground cover estimation or counting the number of surviving plants or tillers in a given area. Those methods are subjective, time-consuming and/or labour intensive. This study aimed to develop a phenomic method to evaluate the persistence of perennial ryegrass cultivars in field plots. Data acquisition was conducted three years after sowing to estimate the persistence of perennial ryegrass using high-resolution aerial-based multispectral and ground-based red, green and blue(RGB) sensors, and subsequent image analysis. There was a strong positive relationship between manual ground cover and sensor-based ground cover estimates (p < 0.001). Although the manual plant count was positively correlated with sensor-based ground cover (p < 0.001) intra-plot plant size variation influenced the strength of this relationship. We conclude that object-based ground cover estimation is most suitable for use in large-scale breeding programs due to its higher accuracy, efficiency and repeatability. With further development, this technique could be used to assess temporal changes of perennial ryegrass persistence in experimental studies and on a farm scale. Full article
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland and Forage Production)
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14 pages, 3345 KiB  
Article
Estimating Biomass of Black Oat Using UAV-Based RGB Imaging
by Matheus Gabriel Acorsi, Fabiani das Dores Abati Miranda, Maurício Martello, Danrley Antonio Smaniotto and Laercio Ricardo Sartor
Agronomy 2019, 9(7), 344; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy9070344 - 29 Jun 2019
Cited by 31 | Viewed by 5025
Abstract
The spatial and temporal variability of crop parameters are fundamental in precision agriculture. Remote sensing of crop canopy can provide important indications on the growth variability and help understand the complex factors influencing crop yield. Plant biomass is considered an important parameter for [...] Read more.
The spatial and temporal variability of crop parameters are fundamental in precision agriculture. Remote sensing of crop canopy can provide important indications on the growth variability and help understand the complex factors influencing crop yield. Plant biomass is considered an important parameter for crop management and yield estimation, especially for grassland and cover crops. A recent approach introduced to model crop biomass consists in the use of RGB (red, green, blue) stereo images acquired from unmanned aerial vehicles (UAV) coupled with photogrammetric softwares to predict biomass through plant height (PHT) information. In this study, we generated prediction models for fresh (FBM) and dry biomass (DBM) of black oat crop based on multi-temporal UAV RGB imaging. Flight missions were carried during the growing season to obtain crop surface models (CSMs), with an additional flight before sowing to generate a digital terrain model (DTM). During each mission, 30 plots with a size of 0.25 m² were distributed across the field to carry ground measurements of PHT and biomass. Furthermore, estimation models were established based on PHT derived from CSMs and field measurements, which were later used to build prediction maps of FBM and DBM. The study demonstrates that UAV RGB imaging can precisely estimate canopy height (R2 = 0.68–0.92, RMSE = 0.019–0.037 m) during the growing period. FBM and DBM models using PHT derived from UAV imaging yielded R2 values between 0.69 and 0.94 when analyzing each mission individually, with best results during the flowering stage (R2 = 0.92–0.94). Robust models using datasets from different growth stages were built and tested using cross-validation, resulting in R2 values of 0.52 for FBM and 0.84 for DBM. Prediction maps of FBM and DBM yield were obtained using calibrated models applied to CSMs, resulting in a feasible way to illustrate the spatial and temporal variability of biomass. Altogether the results of the study demonstrate that UAV RGB imaging can be a useful tool to predict and explore the spatial and temporal variability of black oat biomass, with potential use in precision farming. Full article
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland and Forage Production)
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14 pages, 2103 KiB  
Article
Field Spectroscopy to Determine Nutritive Value Parameters of Individual Ryegrass Plants
by Chaya Smith, Noel Cogan, Pieter Badenhorst, German Spangenberg and Kevin Smith
Agronomy 2019, 9(6), 293; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy9060293 - 06 Jun 2019
Cited by 14 | Viewed by 3287
Abstract
The nutritive value (NV) of perennial ryegrass is an important driver of productivity for grazing stock; therefore, improving NV parameters would be beneficial to meat and dairy producers. NV is not actively targeted by most breeding programs due to NV measurement being prohibitively [...] Read more.
The nutritive value (NV) of perennial ryegrass is an important driver of productivity for grazing stock; therefore, improving NV parameters would be beneficial to meat and dairy producers. NV is not actively targeted by most breeding programs due to NV measurement being prohibitively slow and expensive. Nondestructive spectroscopy has the potential to reduce the time and cost required to screen for NV parameters to make targeted breeding of NV practical. The application of a field spectrometer was trialed to gather canopy spectra of individual ryegrass plants to develop predictive models for eight NV parameters for breeding programs. The targeted NV parameters included acid detergent fibre, ash, crude protein, dry matter, in vivo dry matter digestibility, in vivo organic matter digestibility, neutral detergent fibre, and water-soluble carbohydrates. The models were developed with partial least square regression. Model predicted ranking of plants had R2 between (0.87 and 0.39) and lab rankings of highest preforming plants. The highest ranked plants, which are generally the selection target for breeding programs, were accurately identified with the canopy-based model at a speed, cost and accuracy that is promising for NV breeding programs. Full article
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland and Forage Production)
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16 pages, 2190 KiB  
Article
Biomass Prediction of Heterogeneous Temperate Grasslands Using an SfM Approach Based on UAV Imaging
by Esther Grüner, Thomas Astor and Michael Wachendorf
Agronomy 2019, 9(2), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy9020054 - 26 Jan 2019
Cited by 76 | Viewed by 6639
Abstract
An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and [...] Read more.
An early and precise yield estimation in intensive managed grassland is mandatory for economic management decisions. RGB (red, green, blue) cameras attached on an unmanned aerial vehicle (UAV) represent a promising non-destructive technology for the assessment of crop traits especially in large and remote areas. Photogrammetric structure from motion (SfM) processing of the UAV-based images into point clouds can be used to generate 3D spatial information about the canopy height (CH). The aim of this study was the development of prediction models for dry matter yield (DMY) in temperate grassland based on CH data generated by UAV RGB imaging over a whole growing season including four cuts. The multi-temporal study compared the remote sensing technique with two conventional methods, i.e., destructive biomass sampling and ruler height measurements in two legume-grass mixtures with red clover (Trifolium pratense L.) and lucerne (Medicago sativa L.) in combination with Italian ryegrass (Lolium multiflorum Lam.). To cover the full range of legume contribution occurring in a practical grassland, pure stands of legumes and grasses contained in each mixture were also investigated. The results showed, that yield prediction by SfM-based UAV RGB imaging provided similar accuracies across all treatments (R2 = 0.59–0.81) as the ruler height measurements (R2 = 0.58–0.78). Furthermore, results of yield prediction by UAV RGB imaging demonstrated an improved robustness when an increased CH variability occurred due to extreme weather conditions. It became apparent that morphological characteristics of clover-based canopies (R2 = 0.75) allow a better remotely sensed prediction of total annual yield than for lucerne-grass mixtures (R2 = 0.64), and that these crop-specific models cannot be easily transferred to other grassland types. Full article
(This article belongs to the Special Issue Remote Sensing Application for Monitoring Grassland and Forage Production)
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