Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review
The potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield and quality traits directly impacts the fertilization and irrigation practises and frequency...
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MDPI AG
2025-01-01
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| author | Abid Ali Hans-Peter Kaul |
| author_facet | Abid Ali Hans-Peter Kaul |
| author_sort | Abid Ali |
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| description | The potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield and quality traits directly impacts the fertilization and irrigation practises and frequency of utilization (cuts) in grasslands. Therefore, the main goal of the review is to examine the techniques for using PA applications to monitor productivity and quality in forage and grasslands. To achieve this, the authors discuss several monitoring technologies for biomass and plant stand characteristics (including quality) that make it possible to adopt digital farming in forages and grassland management. The review provides an overview about mass flow and impact sensors, moisture sensors, remote sensing-based approaches, near-infrared (NIR) spectroscopy, and mapping field heterogeneity and promotes decision support systems (DSSs) in this field. At a small scale, advanced sensors such as optical, thermal, and radar sensors mountable on drones; LiDAR (Light Detection and Ranging); and hyperspectral imaging techniques can be used for assessing plant and soil characteristics. At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. It is known that the delineation of sward heterogeneity is more difficult in mixed grasslands due to spectral similarity among species. Thanks to Diversity-Interactions models, jointly assessing various species interactions under mixed grasslands is allowed. Further, understanding such complex sward heterogeneity might be feasible by integrating spectral un-mixing techniques such as the super-pixel segmentation technique, multi-level fusion procedure, and combined NIR spectroscopy with neural network models. This review offers a digital option for enhancing yield monitoring systems and implementing PA applications in forages and grassland management. The authors recommend a future research direction for the inclusion of costs and economic returns of digital technologies for precision grasslands and fodder production. |
| format | Article |
| id | doaj-art-15efd4d9fa52438e994950fdb5f52f9e |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-15efd4d9fa52438e994950fdb5f52f9e2025-01-24T13:47:59ZengMDPI AGRemote Sensing2072-42922025-01-0117227910.3390/rs17020279Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A ReviewAbid Ali0Hans-Peter Kaul1Department of Agricultural and Food Sciences, University of Bologna, Viale Fanin 44, 40127 Bologna, ItalyDepartment of Crop Sciences, Institute of Agronomy, University of Natural Resources and Life Sciences Vienna (BOKU), 3430 Tulln, AustriaThe potential of precision agriculture (PA) in forage and grassland management should be more extensively exploited to meet the increasing global food demand on a sustainable basis. Monitoring biomass yield and quality traits directly impacts the fertilization and irrigation practises and frequency of utilization (cuts) in grasslands. Therefore, the main goal of the review is to examine the techniques for using PA applications to monitor productivity and quality in forage and grasslands. To achieve this, the authors discuss several monitoring technologies for biomass and plant stand characteristics (including quality) that make it possible to adopt digital farming in forages and grassland management. The review provides an overview about mass flow and impact sensors, moisture sensors, remote sensing-based approaches, near-infrared (NIR) spectroscopy, and mapping field heterogeneity and promotes decision support systems (DSSs) in this field. At a small scale, advanced sensors such as optical, thermal, and radar sensors mountable on drones; LiDAR (Light Detection and Ranging); and hyperspectral imaging techniques can be used for assessing plant and soil characteristics. At a larger scale, we discuss coupling of remote sensing with weather data (synergistic grassland yield modelling), Sentinel-2 data with radiative transfer modelling (RTM), Sentinel-1 backscatter, and Catboost–machine learning methods for digital mapping in terms of precision harvesting and site-specific farming decisions. It is known that the delineation of sward heterogeneity is more difficult in mixed grasslands due to spectral similarity among species. Thanks to Diversity-Interactions models, jointly assessing various species interactions under mixed grasslands is allowed. Further, understanding such complex sward heterogeneity might be feasible by integrating spectral un-mixing techniques such as the super-pixel segmentation technique, multi-level fusion procedure, and combined NIR spectroscopy with neural network models. This review offers a digital option for enhancing yield monitoring systems and implementing PA applications in forages and grassland management. The authors recommend a future research direction for the inclusion of costs and economic returns of digital technologies for precision grasslands and fodder production.https://www.mdpi.com/2072-4292/17/2/279moisture sensorsmowing eventsimpact sensorsnear-infrared spectroscopyremote sensinggrassland |
| spellingShingle | Abid Ali Hans-Peter Kaul Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review Remote Sensing moisture sensors mowing events impact sensors near-infrared spectroscopy remote sensing grassland |
| title | Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review |
| title_full | Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review |
| title_fullStr | Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review |
| title_full_unstemmed | Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review |
| title_short | Monitoring Yield and Quality of Forages and Grassland in the View of Precision Agriculture Applications—A Review |
| title_sort | monitoring yield and quality of forages and grassland in the view of precision agriculture applications a review |
| topic | moisture sensors mowing events impact sensors near-infrared spectroscopy remote sensing grassland |
| url | https://www.mdpi.com/2072-4292/17/2/279 |
| work_keys_str_mv | AT abidali monitoringyieldandqualityofforagesandgrasslandintheviewofprecisionagricultureapplicationsareview AT hanspeterkaul monitoringyieldandqualityofforagesandgrasslandintheviewofprecisionagricultureapplicationsareview |