A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland

Abstract

The development of precision grass measurement technologies is of vital importance to securing the future sustainability of pasture-based livestock production systems. There is potential to increase grassland production in a sustainable manner by achieving a more precise measurement of pasture quantity and quality. This review presents an overview of the most recent seminal research pertaining to the development of precision grass measurement technologies. One of the main obstacles to precision grass measurement, sward heterogeneity, is discussed along with optimal sampling techniques to address this issue. The limitations of conventional grass measurement techniques are outlined and alternative new terrestrial, proximal, and remote sensing technologies are presented. The possibilities of automating grass measurement and reducing labour costs are hypothesised and the development of holistic online grassland management systems that may facilitate these goals are further outlined.

Assessment of remotely sensed inventories for land cover classification of public grasslands in Manitoba, Canada

“…Error from reproducibility bias can be reduced by adhering to a robustly designed sampling protocol (Murphy et al, 2021). Developing a comprehensive grassland inventory would present new opportunities for evaluating the value of ecosystem services of grasslands, such as carbon sequestration and forage production.…”

“…To prevent any further losses or degradation of grasslands in the Canadian Prairies, it is critical to monitor and assess grassland dynamics to determine stewardship and conservation activities. Traditional methods of monitoring grassland cover, such as biomass clipping, visual expert assessment and rising plate meter, are costand time-prohibitive because field measurements can only be taken on a local scale and require a large number of field researchers to undertake a regional survey (Ali et al, 2016;Murphy et al, 2021).…”

“…To prevent any further losses or degradation of grasslands in the Canadian Prairies, it is critical to monitor and assess grassland dynamics to determine stewardship and conservation activities. Traditional methods of monitoring grassland cover, such as biomass clipping, visual expert assessment and rising plate meter, are cost‐ and time‐prohibitive because field measurements can only be taken on a local scale and require a large number of field researchers to undertake a regional survey (Ali et al, 2016; Murphy et al, 2021). Remote sensing technology allows for estimating vegetation distribution and productivity without the need for physical contact, thus, addressing the shortcomings of traditional methods while being able to detect immediate changes over a large area (Reinermann et al, 2020).…”

Comparison of EO4Agri Recommendations With In-Depth Literature Review

“…216 [27], [28], [29], [30] Data for producing maps of basic fertilizers 1600 [31] Data for production maps of fertilizer in the phenophase 30-34 3 -Data for determining heights of crops 846 [32], [33], [34], [35] Data for estimating the extent of diseases or damages (losses) 2 850 [36], [37] Data for monitoring hydrological stresses 426 [38], [39], [40] Data for producing exact information about climatic changes 2650 [18], [41], [42], [43], [44] Data for identification parcels for potential land for biomass production 146 -Data for creating flood maps (for Q5,25,50,100years) 1230 [45], [46], [47] Data for annual soil erosion risk maps 250 if special search "soil erosion" [48], [49] Data to produce maps of the occurrence of diseases 1750 special search [50], [51] Data for the production of actual calamities map (droughts, flood, fires, earthquakes, ...) 37 [45], [52], [53] Data for production maps of relevant information for biofuel production 248 [54], [55], [56], [57] Data for determining productivity of grassland and pastures. 205 [58], [59], [60] Data for support of the Common Agricultural Policy new 'greening' rules, crop, ecologically sensitive areas 127 [61] Data for the identification of crops to control subsidies 1110 -Data for water protection against nitrates 1130 …”

Herbage Mass, N Concentration, and N Uptake of Temperate Grasslands Can Adequately Be Estimated from UAV-Based Image Data Using Machine Learning

“…Furthermore, a variety of vehicle-mounted or handheld proximal sensing applications exist to derive estimates of biomass and plant nutrient status exploiting canopy reflectance, such as the Yara N-Sensor [26,27], Trimble GreenSeeker [28], or general field spectroradiometers [29,30]. These methods have limitations, such as insufficiently representing spatial variability, allowing operator bias, requiring a high number of repetitions, and needing vehicles and direct access to the field, thereby potentially disturbing the canopy [31].…”