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

Darren J. Murphy; Michael D. Murphy; Bernadette O’Brien; Michael O’Donovan

doi:10.3390/agriculture11070600

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

Murphy, Darren J.;Murphy, Michael D.;O’Brien, Bernadette;O’Donovan, Michael 2021-06-28 00:00:00 agriculture Review A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland 1 , 2 2 1 1 , Darren J. Murphy , Michael D. Murphy , Bernadette O’Brien and Michael O’Donovan * Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Ireland; darren.murphy@teagasc.ie (D.J.M.); bernadette.obrien@teagasc.ie (B.O.) Department of Process, Energy and Transport Engineering, Munster Technological University, Cork, Ireland; michaeld.murphy@cit.ie * Correspondence: michael.odonovan@teagasc.ie 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. Keywords: pasture-based agriculture; precision agriculture; remote sensing; spectroscopy; grass Citation: Murphy, D.J.; Murphy, measurement; grassland sampling M.D.; O’Brien, B.; O’Donovan, M. A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland. Agriculture 2021, 11, 600. https://doi.org/10.3390/ 1. Introduction agriculture11070600 Demand for animal protein products is predicted to increase by >70% in the coming decades as a consequence of the growing distribution of wealth in developing coun- Academic Editor: Dionissios Kalivas tries [1,2]. Consequently, this could potentially result in an 80% increase in agricultural GHG emissions, which would critically impact the environment if not mitigated [3]. Grass- Received: 7 June 2021 land based agriculture has a signiﬁcant role to play in terms of increasing food production Accepted: 18 June 2021 in an environmentally sustainable manner. Over recent decades, there has been a 30% Published: 28 June 2021 decrease in European pasture land usage as a result of the increased levels in production ef- ﬁciency and feed controllability that are achievable with conﬁnement based systems, where Publisher’s Note: MDPI stays neutral animals are fed indoors [4,5]. Conversely, pasture-based systems in suitable climates have with regard to jurisdictional claims in the potential to be more economically and environmentally sustainable than conﬁnement published maps and institutional afﬁl- systems. However, pasture-based systems are hindered by reduced feed controllability iations. due to the spatial and temporal heterogeneity of grassland swards [6,7]. The quantity of herbage available for grazing within pastures can vary between 15% and 60% as a result of selective grazing, dung pats, and seasonal changes in sward morphology, making it difﬁcult to accurately measure and allocate for grazing [8–10]. Copyright: © 2021 by the authors. In Ireland, grazed grass is the predominant livestock feed source due to the suitability Licensee MDPI, Basel, Switzerland. of the temperate climate for grass production [11]. The Irish climate provides optimum This article is an open access article conditions for grazing, enabling cows to graze in excess of 300 days per year, which distributed under the terms and allows Ireland to produce milk and beef at a relatively low cost and in a sustainable conditions of the Creative Commons manner [12,13]. Maximising pasture utilisation through optimal grassland management is Attribution (CC BY) license (https:// vital in terms of ensuring the economic sustainability and mitigation of the environmental creativecommons.org/licenses/by/ impact of pasture-based livestock production. A pasture-based system that can maintain 4.0/). Agriculture 2021, 11, 600. https://doi.org/10.3390/agriculture11070600 https://www.mdpi.com/journal/agriculture Agriculture 2021, 11, 600 2 of 38 concentrate and nitrogen (N) fertilizer levels while increasing grass utilisation and stocking rate will increase N use efﬁciency [14,15]. Efﬁcient and sustainable pasture-based livestock production is primarily based upon synchronising the herd’s dietary requirements with seasonal grass production rates. This allows for the maximum level of fresh grass to be utilised through an increased daily intake of high-quality fresh grass dry matter (DM) per grazing animal [16,17]. Increasing grass utilisation has major ﬁnancial beneﬁts, as fresh grass is the cheapest feed source on Irish ruminant livestock farms [18]. Currently, the average Irish dairy farmer is utilising approximately 7–8 t DM ha of grass per annum, but has the potential to utilise 12–16 t DM ha [19–21]. The value of increasing 1 1 1 grass utilisation has been estimated to be up to 173 tonne ha year [17]. Frequent and accurate measurement of grass quantity and quality is one of the main methods of maximising grass utilisation and production on pasture-based farms [20,22,23]. Optimal grassland management is highly dependent on the accuracy of information on pasture quantity and quality that is available to the farmer [16,24]. Precise grass allocation to the herd is integral to optimal grassland management. Excess allocation of grass leads to wastage and quality degradation within a pasture. Alternatively, not providing sufﬁcient herbage to the herd results in decreased milk and beef production [25]. Grass is quantiﬁed and allocated in terms of herbage mass (HM), which is the unit weight of DM per hectare 1 1 (ha ) and is measured in units of kg DM ha . Several non-destructive methods and tools for measuring grass quantity have be- come popular on Irish farms in recent decades. There are a range of issues with these methods with regard to operator bias, precision, and difﬁculties in accounting for spatial variation [26–28]. Another signiﬁcant issue concerning current grass measurement prac- tices is the absence of a deﬁnitive protocol for grass measurement that farmers can use to objectively measure their grass and to account for the variation of grass growth within paddocks [29]. In terms of grass quality, there are no established on-farm methods which a farmer can use to estimate the quality of grass within their pasture. Pasture qualitative analysis methods are typically laboratory based and involve time consuming pre-processing proce- dures, such as grinding and oven drying, which can take several days to complete. Grass quality parameters that are considered important for grazing systems include DM, dry matter digestibility (DMD), metabolisable energy (ME), organic matter digestibility (OMD), crude protein (CP) and water-soluble carbohydrates (WSC) [11,30]. Signiﬁcant potential exists for improving the availability and accuracy of grassland measurement information by means of precision agriculture (PA) technologies. The concept of PA is focused on the acquisition of precise ﬁeld data at a spatial and temporal scale that would capture pasture variation and enable targeted responses, with the aim of increasing economic returns and reducing environmental impacts [31]. Precision technologies are a relatively new phe- nomenon with regard to grassland management compared with larger, more industrial scale agri-systems such as cropland industries [24]. Schellberg et al. [31] outlined reasons for the comparatively slow uptake of PA technologies with regard to grassland farming. The most signiﬁcant factors included the greater diversity within grassland, in terms of the spatial variation of soil and pasture characteristics, and the highly temporal dynamics of grass species. The aim of this review is to present an overview of the most seminal research pertain- ing to recent precision grassland measurement technological developments. The develop- ment of such technologies will be integral to achieving sustainable growth in grassland livestock industries in the future. This review is primarily focused on grass measurement systems that may be suitable for pasture-based livestock systems situated in temperate regions such as Ireland, although the research discussed is also applicable to global pasture- based industries. There has been no recent review of grass measurement technological developments relevant to Irish grassland. Relevant literature was initially collected using online databases prior to manual screening to select the most seminal research for inclusion in this review. Agriculture 2021, 11, 600 3 of 38 The ﬁrst section of this review outlines the methodology used to select the literature discussed in the subsequent sections. Secondly, signiﬁcant factors pertaining to grassland sward heterogeneity, conventional grass measurement methods, and the principles of pasture sampling will be discussed. Thirdly, this review will focus on state-of-the-art research on precision grassland measurement technologies. Finally, current challenges facing the development of precision grass measurement systems and the future of grassland measurement will be discussed. 2. Review Search Methodology and Literature Summary Searches for seminal literature were performed on Google Scholar [32] and ScienceDi- rect [33] using the following keywords: grass measurement, pasture, remote sensing, temperate grassland, perennial rye grass, clover, grassland heterogeneity, spectroscopy. Initially, studies from the period of 1970–2021 were selected (n = 313) to track developments in conventional grass measurement over the past 50 years and provide the context for the initial sections of this review. A subset of more recent studies from the period between 2001–2021 (n = 47) was then selected to identify seminal research relating to state-of-the-art developments in grass measurement technology, which are discussed in the latter sections of this review. The literature dataset was then manually reﬁned to exclude non relevant and duplicate studies. Inclusion criteria were: studies must contain original peer-reviewed research and be published as either scientiﬁc journal articles or conference papers; research was conducted on temperate pasture, preferably including perennial rye grass perennial ryegrass (Lolium perenne L.) (PRG) and/or white clover (Trifolium repens L.; clover) (WC) and articles were in the English language. Exclusion criteria included research conducted on arid or tropical grassland with no relevance to Irish pasture and studies that had insufﬁcient information regarding the error of assessed measurement systems. Additional studies were located by tracking references and citations from the most relevant literature in the initial dataset. The reﬁned literature dataset contained n = 99 studies relating to the measurement of temperate grassland. A summary of the dataset indicating the technologies used, region, grass species and scale of the selected studies can be viewed in Appendix A. The selected studies are discussed in the following sections of this review. 3. Grassland Sward Heterogeneity The availability of herbage for grazing can vary considerably within pastures, which makes it difﬁcult to accurately quantify and allocate on a regular basis. Sward heterogeneity in terms of both quantity and quality can increase as a result of a number of factors, including soil, environmental, temporal, compositional, and grazing conditions. Jordan et al. [34] recorded mean variation in HM to be in the range of 15–30% on intensively cut PRG dominant silage ﬁelds in the North of Ireland. This study further recorded increases in sward variation as the growing season progressed in accordance with the morphological growth stages of PRG. Heterogeneity is typically higher within grazed pastures compared with silage ﬁelds, due to selective grazing by animals, which increases the difﬁculty of estimating average HM [9]. Barthram et al. [8] recorded variation in sward height in the range of 30–70% due to selective grazing on PRG dominant swards grazed by sheep in Scotland. Klootwijk et al. [9] quantiﬁed that the area of rejected patches of pasture ranged from 22% to 43%, which increased as the grazing season progressed in Dutch PRG pastures, and recommended that the area of rejected patches be accounted for when calculating available HM. Murphy et al. [35] found that average pre-grazing HM variation was 36% over a grazing season within Irish PRG dominant dairy pastures. A summary of reported values of sward heterogeneity in terms of grass quantity can be seen in Table 1. A further cause of pasture variation and damage is poaching. In wet conditions, tread- ing pressure from animals remoulds the soil surface damaging the sward and compacting the soil, which can increase weed ingress and reduce pasture production. Grassland man- agement factors, such as stocking rate and herbage allowance, also have signiﬁcant impacts on the variation of sward yield and quality within a pasture [36]. Sward composition, in Agriculture 2021, 11, 600 4 of 38 terms of both species and morphology, is another signiﬁcant factor with regard to pasture heterogeneity. Mixed swards of WC and PRG are becoming more popular on Irish farms due to their noted environmental and grazing beneﬁts [37,38]. Clover has a lower canopy height than PRG, resulting in mixed swards having greater variation in canopy surface height, which can make measurement more difﬁcult [39]. Multi-species swards, including plants such as chicory (Cichorium intybus L.) and plantain (Plantago lanceolata L.), may become more frequently used on grassland pastures in the near future with the objectives of increasing quality and reducing N requirements [40]. Similar to PRG/WC swards, multi-species swards may have greater variation in canopy height and structure compared with PRG monocultures. Furthermore, the morphological growth stage of the PRG plant has a major effect on sward structure and variation. The main components of the PRG plant (leaf, stem, and dead leaf proportions) vary considerably depending on the morphology of the plant, time of year, and grazing management [41]. Temporal and morphological effects have further signiﬁcant impacts on sward quality variation. Wilkinson et al. [23] found that variation in most sward quality components increased rapidly as the sward entered its reproductive growth stage, with variation at a maximum in the middle of the grazing season in British pastures. The study also found that within month variation in sward quality was large, resulting in either inadequate or excessive amounts of essential nutrients being provided to grazing animals and recommended that regular sward quality measurements be taken to allow for more optimum feeding of animals. Table 1. Summary of reported values of mean sward heterogeneity in terms of pre-grazing grass quantity on temper- ate grasslands. Grass Measurement Sward Study Year Region Sward Type Species Parameter Heterogeneity * Murphy et al. [29] 2020 PRG/WC Ireland Dairy pasture HM (kg DM ha ) 36% Jordan et al. [34] 2003 PRG Ireland Silage ﬁeld 25% HM (kg DM ha ) Murphy et al. [29] 2020 PRG/WC Ireland Dairy pasture CSH (mm) 29% Klootwijk et al. [9] 2019 PRG The Netherlands Dairy pasture CSH (mm) 28% Barthram et al. [8] 2005 PRG/WC Scotland Sheep pasture Height (mm) 46% * Sward heterogeneity = coefﬁcient of variation of measurement parameter, HM = herbage mass, CSH = compressed sward height, Height = standing sward height, PRG = perennial rye grass, WC = white clover. 4. Conventional Grass Measurement Destructive measurement refers to when herbage is cut and removed from the pasture for direct analysis. Destructive techniques are typically used as reference methods for modelling herbage parameters by means of non-destructive measurement methods. The ‘gold standard’ method of determining HM is by cutting and weighing herbage samples using a quadrat, shears, and scales [16,42]. Despite cutting and weighing being the refer- ence method for determining HM there are numerous potential sources of measurement error including operator bias regarding sample area selection and post cutting height. Furthermore, there are several well documented disadvantages to cutting and weighing including labour intensity and herbage destruction [42–44]. A signiﬁcant disadvantage of cutting and weighing is the requirement of a large number of samples to account for sward spatial variation within grazed pastures. Sward heterogeneity can be accounted for by increasing sampling intensity. However, this leads to increases in measurement labour and time, as well as increases in the quantity of herbage removed from the pasture [45–47]. Non-destructive measurement refers to when grass is analysed in-situ and modelling techniques are used to predict selected parameters. This form of measurement enables real- time analysis of pasture and ultimately allows for more responsive grassland management decision making. Non-destructive measurement techniques are typically cheaper, less laborious, and more practical than destructive methods. For these reasons non-destructive Agriculture 2021, 11, 600 5 of 38 techniques are more commonly used by farmers on a regular basis. However, modelling techniques are prone to error and non-destructive methods are typically less accurate than destructive methods. Visual estimation is the most fundamental method of non-destructive grass measure- ment. It involves the farmer observing the pasture and estimating the average HM within a paddock. It is the fastest, cheapest, and least laborious method of measuring HM. The farmer is able to use their knowledge of the sward’s composition to account for the varia- tion in HM within the pasture [48,49]. The most signiﬁcant issue with visual estimation is that it is highly subjective and variations in herbage estimations between observers have been noted to be large [50–52]. The most established non-destructive tool for measuring pasture in Ireland is the rising plate meter (RPM) [20,29]. The RPM records a combined measure of pasture height and density, referred to as compressed sward height (CSH), using a weighted disc attached to a scaled staff that is dropped onto the sward. Recorded CSH is then used to model HM. Use of the RPM requires minimal training and a large number of samples can be recorded and distributed throughout a paddock in a relatively short time duration [53,54]. A recent iteration of the RPM has been developed in Ireland [55] that uses a GPS integrated ultrasonic sensor to record the height of the rising plate (Figure 1). The main advantages of this RPM over conventional models are its rapid data processing capabilities via automated links to online decision support tools (DST) and its ability to geo-tag measurement data. Figure 1. Schematic of ultrasonic rising plate meter developed by McSweeney et al. [55]. Despite the RPM being an established grass measurement tool, its limitations in terms of accuracy have been noted. A considerable source of RPM error is the large variation be- tween CSH measurements recorded within pastures, resulting from the interaction between the rising plate and the heterogeneity of the vertical proﬁle of the sward. Factors reported to affect this interaction include grass species, season, and grazing intensity [28,47,56]. There is no standardised RPM design and models vary considerably in terms of plate pressure and measurement system. This makes it difﬁcult to transfer established HM cali- brations between different RPM models [57]. Despite the RPM being designed to reduce the subjectivity of grass measurement, there is no robust measurement protocol on how to use the RPM in an objective manner and this can contribute to measurement variation. A recent study by Togeiro de Alckmin et al. [58] on controlled PRG trial plots in Tasmania found that the RPM had a root mean square error (RMSE) of 522 kg DM ha . A similar Agriculture 2021, 11, 600 6 of 38 study in Ireland on PRG dominant trial plots and grazed paddocks reported RPM errors 1 1 of 354 kg DM ha and stated that this error could be reduced to 243 kg DM ha by combining RPM measurement with grassland management and meteorological data by means of machine learning [59]. The study further included a comprehensive review of RPM HM prediction error and its sources. A further study estimated the combined effects of both measurement and calibration error for the RPM to be 28.1% relative prediction error (RPE), when a robust measurement protocol was adhered to [35]. 5. Pasture Sampling Techniques To account for pasture heterogeneity multiple samples or measurements may need to be taken at locations distributed throughout a paddock following a predetermined protocol [34,45,60]. The effectiveness of a sampling protocol can be deﬁned by its accuracy, precision, and level of potential bias [43]. To determine an absolute mean parameter value for a pasture, the entirety of the herbage within that pasture would need to be harvested and analysed. This may be possible on small, controlled trial plots used in research but is not practical on grazed paddocks. Therefore, the best possible representation of the absolute mean must be determined, henceforth referred to as the ‘true’ mean. Accurately estimating the ‘true’ mean of any herbage parameter can be difﬁcult owing to the heterogeneous nature of grazed swards. A signiﬁcant source of measurement error is inconsistent operator use, which is deﬁned in terms of reproducibility or operator bias [61]. Bias error can be minimised by adhering to a robustly designed sampling protocol. Once a pasture measurement tool is used in accordance with manufacturer guidelines, bias in terms of sample area selection remains the greatest source of unknown bias. For example, when measuring a pasture area, an operator may select the shortest path between the pasture entry and exit points and take all of their samples along this path, as this is most convenient. This path may not give an accurate representation of the variation of herbage within the pasture and is therefore biased by the operator ’s desire for convenience. Likewise, the operator may consciously or subconsciously select sample locations with either consistently high or low proportions of herbage. Similarly, an operator might choose to sample a paddock along transect lines (Figure 2a) in an attempt to distribute samples more evenly. This method is also biased by the operators preference with regard to the positioning of each transect line. There is no deﬁnitive protocol for objective pasture sampling or measurement on Irish pastures. With regard to the RPM, measurements are typically carried out 25–50 times in transects or in a ‘W’ pattern (Figure 2b) throughout a paddock [27,43,52]. To avoid operator bias and maximise measurement precision, sample locations should be randomly selected and spatially balanced throughout a pasture, although this can be difﬁcult to implement in practice. If sample location selections are totally random, the entire area within a paddock has an equal probability of selection. Measurement parameter values can be treated as random variables and statistical analysis can be employed to determine parameter mean and estimation error without bias [44,62]. Increasing sampling area and resolution may increase measurement precision, how- ever, this further increases sampling time and cost. There is a trade-off between the beneﬁt of increasing accuracy versus time and cost. Reducing measurement time and effort is vital, not only in saving labour costs for farmers, but also to encourage more farmers to measure grass on a regular basis. The time and cost requirements of regular and accurate grass measurement are signiﬁcant barriers to promoting grass measurement on farms. A study conducted by Creighton et al. [21] showed that only 20% of Irish dairy farmers used technology to measure grass on a regular basis. Deming et al. [63], in a study of Irish dairy farms that were classiﬁed as labour efﬁcient, found that farmers spent between 0.28 1 1 and 0.41 h cow year at grass measurement. Behavioural studies by Hall et al. [64] in Tasmania and Eastwood et al. [65] in New Zealand, reported that farmers reported a lack of conﬁdence in accuracy and regarded measurement time and effort as major barriers to the adoption of measurement tools for pasture management. Agriculture 2021, 11, 600 7 of 38 Figure 2. Illustration of (a) ‘X’ transect; (b) ‘lazy W’; (c) simple random; and (d) random stratiﬁed sampling pasture measurement protocols on 1 ha grazed pasture, with orange circles indicating measurement locations (n = 20) and blue dashed line outlining the measurement route for (a) and (b). The requirement for the development of a universal pasture sampling methodology to reduce operator bias, give more precise representations of spatial variation, and min- imise measurement labour has long been acknowledged [34,46,56]. O’ Sullivan et al. [54] presented a combined technique of quadrat cuts and RPM measurements with the aim of reducing the number of herbage cuts required (by 50%) to accurately predict ‘true’ HM for research purposes on Irish PRG pastures. Thomson et al. [52] outlined the need for HM measurement protocol standardisation between dairy research centres in New Zealand and recommended that 50–80 RPM measurements be taken per paddock. Nakagami [10] developed a method to assess HM in Japanese pastures by RPM sampling just two areas per paddock, but when validated on commercial paddocks, only half of the estimates were found to be within 20% of ‘true’ mean. Hutchinson et al. [66] prototyped a pasture sampling protocol for the RPM in the form of a decision tree that could be easily understood by farmers, outlining the required number of RPM measurements in relation to an operators desired level of precision. The study found that a depreciating exponential relationship existed between RPM measurement rate and HM prediction error and recommended random stratiﬁed sampling (RSS) as an accurate method of pasture sampling. Similar rela- tionships between grass sampling rate and error have been reported by Jordan et al. [34], O’ Sullivan et al. [54], and Murphy et al. [29] on Irish PRG swards. Using quadrat cuts, Jordan et al., [34] recommended a sampling rate of 7 cuts ha based on the principle of RSS, to estimate ‘true’ mean HM to within 5% error and enable yield mapping of spatial heterogeneity within silage ﬁelds. A study by Murphy et al. [35] utilised RSS to developed a grass measurement optimisation tool to generate accurate and efﬁcient grass measurement protocols and concluded that eight measurements ha was an optimum sampling rate for the RPM. Agriculture 2021, 11, 600 8 of 38 The RSS method involves dividing the target measurement domain into several equally sized strata and then assigning an equal number of samples randomly within each stratum, as seen in Figure 2d. This allows for a more efﬁcient distribution of samples within the domain in comparison with simple random sampling (Figure 2c) and average spatial variation within and across strata can be estimated without bias [43,63,67]. The implemen- tation of robust sampling protocols in conjunction with GPS technology enables the use of geostatistical procedures such as Kriging interpolation, which can be used to develop parameter heat maps of a pasture for spatial analysis and PA applications [63,67]. Accurate geo-referenced measurement information of sward quantity and quality would enable the use of variable rate fertilisation systems to reduce cost, GHG emissions, and nutrient leaching to waterways. Moreover, such data could lead to more precise spatial analysis of sward characteristics and ultimately lead to increases in pasture utilisation [24,68,69]. 6. Grass Quality Analysis by Means of Near Infrared Spectroscopy Most conventional grass quality measurement methods require herbage samples to be taken from the ﬁeld and analysed in the laboratory. One of the more established and rapid methods of herbage quality analyses is near infrared spectroscopy (NIRS). Conventional lab-based NIRS required removal of herbage samples from the ﬁeld and pre-processing of the samples prior to analyses. More recent NIRS developments have focused on reducing the need for sample removal and pre-processing. Sample removal can be avoided by means of in-situ or portable NIRS analysis. The main advantages of NIRS are that it is a more rapid analysis technique and it has no chemical input requirements compared with traditional wet chemistry analysis procedures. Disadvantages include the initial cost of purchasing an NIRS spectrometer and its reliance on chemometric modelling techniques, which are prone to error. Near infrared (NIR) light energy has characteristic wavelengths ranging between approximately 700 and 2500 nm on the electromagnetic spectrum [70,71]. Near infrared spectroscopy analysis measures the absorption rates of low energy infrared light radiation within matter, which are then used to quantify the chemical constituents of said matter by means of empirical modelling methods, referred to as chemometrics. Analyses of dried and milled forage quality by means of NIRS is well established within the agri-food industry [72–74]. More recently, NIRS quality prediction calibrations have been derived for dried and milled grass for research purposes in Ireland, such as identifying desired traits for different grass varieties [30,75,76]. Recent research has focused on applying NIRS to predict quality parameters of fresh herbage with the aim of further reducing laboratory workloads by eradicating the need for sample pre-processing, which can also have detrimental effects on sample composition [77]. Spectroscopic analysis of fresh forages and grasses is largely restricted by the high presence of moisture, which results in large spectral peaks that overshadow spectral identiﬁers for numerous quality traits, such as CP [77–79]. Despite this, breakthroughs have been made with regard to NIRS analyses of fresh forage and grass using conventional NIR instruments. Thomson et al. [80] investigated if a pre-existing fresh grass silage NIRS calibration could predict quality in grass/clover silage samples in the UK. The study found that some parameters such as DMD could be predicted with acceptable accuracy. However, bias for parame- ters such as CP increased with clover content and clover speciﬁc calibrations performed better. Alomar et al. [81] concluded that reﬂectance NIRS could accurately predict the 2 1 compositional components, including DM (R = 0.99, SE = 6.5 g kg ) (SE = standard 2 1 error) and CP (R = 0.91, SE = 18.4 g kg ), of a variety of fresh grass swards in Southern Chile. Dale et al. [82] developed fresh grass NIRS calibrations to investigate optimum sampling and storage techniques on Irish PRG dominant pastures and reported R val- 1 1 ues of 0.92 (SE = 0.95 g kg fresh weight ), 0.90 (SE = 0.543 g kg fresh weight ) and 0.79 (SE = 0.622 g kg fresh weight ) for DM, N and WSC, respectively. Lobos et al. [83] reported good prediction performance (R 0.84) for fresh grass NIRS analysis for param- eters DM (RMSE = 1.13%) and CP (RMSE = 2.22%), in comparison with low prediction performance (R 0.78) for DMD (RMSE = 2.41%), OMD (RMSE = 2.61%), and WSC Agriculture 2021, 11, 600 9 of 38 (RMSE = 0.06%) in Chilean permanent pasture. A summary of the accuracy of relevant NIRS calibrations for grass quality is presented in Table 2. A more recent study by Murphy et al. [84] presented NIRS calibrations that could predict DM with a high degree of accuracy 2 1 2 1 (R = 0.86, SE = 9.46 g kg ) and CP with moderate accuracy (R = 0.84, SE = 20.38 g kg ) in Irish PRG swards. The development of rapid NIRS calibrations to predict fresh grass quality would signiﬁcantly reduce laboratory labour, inputs, and cost. Furthermore, fresh grass NIRS would enable more precise grassland and feed management decisions to be made on a daily basis. Table 2. Summary of NIRS grass quality studies and calibration statistics relevant to temperate grassland presented in Murphy et al. [84]. Sample Error (g Study Analyte Region Species Parameters R RPD No. kg ) Murphy et al. Fresh grass Ireland PRG DM, CP 1812 0.85, 0.84 9.5, 20.4 2.57, 2.37 (2021) Lobos et al. Permanent Fresh grass Chile DM, CP 915 0.93, 0.84 11.3, 22.2 3.7, 2.5 (2019) pasture Parrini et al. Natural Fresh grass Italy DM, CP 100 0.87, 0.88 2.75, 2.14 2.75, 2.26 (2019) pasture Bonnal et al. Mixed Fresh grass France CP 103 0.93 1.55 1.97 (2013) swards Alomar et al. Mixed Fresh grass Chile DM, CP 107 0.99, 0.91 6.55, 18.4 7.15, 3.69 (2009) swards McClure et al. Fresh grass USA Fescue N 31 0.88 6 - (2002) Park et al. Fresh grass silage Ireland - DM, N 136 0.85, 0.78 23.3, 28.1 -, 4.8 (1998) Burns et al. Dried & milled Ireland PRG CP 2076 0.98 5.1 - (2014) grass Jafari et al. Dried & milled Ireland PRG CP 153 0.96 6.8 - (2003) grass 1 2 PRG = perennial rye grass, DM = dry matter, CP = crude protein (g kg DM), N = nitrogen, R = coefﬁcient of determination, Error = standard error of cross-validation, standard error of prediction or root mean squared error depending on study, RPD = ratio of percent deviation, ‘-‘ = denotes where data was not published as part of study. In the past two decades, NIRS technological developments in the area of diode array spectrometers and micro-electric-mechanical-systems (MEMS) have allowed new possibili- ties regarding real-time in-situ NIRS analysis of fresh grass [85,86]. Portable spectrometers have numerous advantages over lab-based systems including, in-situ measurement, lower costs, real-time results and non-destructive sampling. Portable NIRS has noted limitations regarding light noise, particle size, wavelength range and moisture effects [86]. A high speed and durable portable spectrometer has been developed for the selection of grass species for breeding purposes [87]. This NIRS sensor was capable of predicting DM of fresh grass, with an acceptable correlation in relation to wet chemistry analysis (R = 0.73), in real-time and was built into a grass plot harvester. Mendarte et al. [88] outlined the potential for using portable NIRS to determine the quality of standing mountain pasture in the Basque Country, reporting reasonable prediction results for DM (R = 0.82, SECV 1 2 = 0.56 g kg ) (SECV = standard error of cross validation) and CP (R = 0.62, SECV = 1.50 g kg DM) in relation to laboratory reference analysis. Reddersen et al. [79] assessed the use of portable NIRS to evaluate the feed quality of mixed species standing swards in Germany and concluded that it was only capable of predicting approximate values (R = 0.72, SECV = 3.9 g kg DM) of N content, due to the high presence of moisture and low levels of sample homogeneity. Smith et al. [89] used a similar technology in an Australian Agriculture 2021, 11, 600 10 of 38 PRG breeding programme and recommended that portable NIRS was feasible as a high speed and low cost method of evaluating nutritive value for parameters CP, DM, DMD, WSC, acid detergent ﬁbre, and neutral detergent ﬁbre, reporting R values ranging between 0.49 and 0.89 and RMSE values between 1.84% and 3.41%. An issue that constrains the development of portable NIRS applications is that many portable spectrometers on the market are ‘closed box’ systems and researchers do not have access to the calibration data within them [86]. In recent years, an on-line NIRS device for silage and pasture quality assessment has been developed in the UK (NIR4) (Figure 3). The NIR4 is capable of scanning fresh pre-cut grass and uploading the spectral data to the user ’s handheld smart device for rapid analysis, with calibrations for parameters DM, CP, WSC and DMD [90]. However, no published data on the precision of this system could be found in the literature. A study by Patton et al. [91] assessed the efﬁcacy of three portable NIRS sensors from different manufacturers to analyse quality traits of PRG swards in the North of Ireland. They concluded that any of the instruments tested could not replicate quality predictions made from a lab based NIRS spectrometer. Hart et al. [92] reported high levels of systemic error (9–22%) using portable NIRS on Swiss mixed swards. There is considerable scope for portable NIRS applications in grassland farming. More research needs to be performed on environmental, moisture, and sample particle heterogeneity effects to establish the feasibility of portable NIRS. Figure 3. Image of NIR4 grass quality analysis system reprinted from ref. [90]. 7. Terrestrial Sensing In the context of this review, terrestrial sensing refers to non-spectral sensors that interact with the sward at (or close to) ground level. Terrestrial on-the-go soil electrode sensing has been used by Vogel et al. [93] to investi- gate potential relationships between soil PH, moisture content, and the spatial variation of herbage mass on grazed German pasture. The study utilised a tractor mounted Veris mobile sensor platform (Figure 4) for rapid soil analysis and apparent soil electrical conductivity was measured to predict soil moisture content. Agriculture 2021, 11, 600 11 of 38 Figure 4. Tractor mounted Veris mobile sensor platform reprinted from ref. [93] for on-the-go soil analysis on grazed pasture. The use of a sensor to directly measure sward height using ultrasonic waves has been investigated on mixed species German swards by Reddersen et al. [94], who found that it 2 1 predicted HM with reasonable accuracy (R = 0.73–0.76, RMSECV = 0.88–1.17 t DM ha ) (RMSECV = root mean squared error of cross-validation). The study further found that combining ultrasonic sward height (USH) and remote sensing data in a multi-sensor (leaf area index and hyperspectral sensors) approach increased HM prediction accuracy by 30%. An earlier study by Fricke et al. [95] investigated combining USH with GPS on a vehicle for real-time ‘on the go’ measurement and rapid yield mapping of pasture, as seen in Figure 5a. A number of USH measurement limitations were highlighted in that study, including poor precision caused by the wide ultrasonic response area and poor responses to changes in sward geometry and heterogeneity. The study further outlined the potential for combining arrays of low cost USH sensors, which could be ﬁtted onto tractors or mowers to generate cheap and minimal effort HM predictions. Safari et al. [96] compared the use of mobile USH and spectral sensing (Figure 5b) with static sensing, reporting lower prediction accuracy for mobile measurement due to positional errors caused by variation in the ground proﬁle. Moeckel et al. [97] found poor results (R = 0.36–0.74, SE = 675–1118 kg DM ha ) for predicting HM using USH on mixed species swards, reporting high errors in mature swards as a result of patches of rejected grass left after grazing. The study further investigated the potential for combining spectral data from spectrometers and satellites with USH and found that utilising both visible and NIR spectral 2 1 data improved HM prediction performance (R = 0.66–0.88, SE = 485–866 kg DM ha ). A similar USH measurement system that could be ﬁxed to a farm vehicle to measure pasture height while traveling at speeds of 20 km h achieved HM prediction accuracies of 2 1 R = 0.75 and SE = 270–350 kg ha on New Zealand grassland [98]. Apparent advantages of USH sensing for grass measurement are that it is relatively fast, low cost, and simple to implement, with the potential for mobile application. Conversely, limitations exist with regard to the precision of USH as a result of high variation in signal responses to canopy heterogeneity. The C-DAX Pasturemeter is a terrestrial sensing device for predicting HM that has been developed and is in common use in New Zealand. The C-DAX is mounted on wheels and is designed to be towed behind a quad bike at approximate speeds of 20 km/h, as illustrated in Figure 6. This device measures pasture height using light emitting and sensing photodiode arrays. As the C-DAX is towed through the pasture the photodiode sensors record a height proﬁle of the pasture. Studies have concluded that measuring pasture standing height has notable limitations with regard to predicting HM in comparison with the RPM [26,27]. Despite this, the C-DAX has one signiﬁcant advantage over the RPM. The C-DAX is capable of acquiring much more data (200 measurements per second) in a more rapid manner than the RPM without the need of walking [99,100]. King et al. [101] compared the measurement accuracies of the C-DAX and RPM over a range of pastures in New Zealand throughout a single grazing season. Results in terms of RMSE ranged 1 1 between 576 and 655 kg DM ha for the C-DAX and 441 and 552 kg DM ha for the Agriculture 2021, 11, 600 12 of 38 RPM. Oudshoorn et al. [102] discovered that the C-DAX predicted HM to within acceptable accuracy (R = 0.76) on Danish PRG/WC swards. The prediction error calculated by Schori [103] was slightly higher for the C-DAX (SE = 311 kg DM ha ) compared with the RPM (SE = 285 kg DM ha ), on Swiss mixed swards over three grazing seasons. The C-DAX also has in-built GPS geo-tagging capabilities, which have been utilised to generate yield maps for targeted pasture management applications [104]. Currently, the C-DAX is not commonly used by Irish grassland farmers. This may be due to a perception that predicting HM by measuring standing sward height is not as accurate as CSH because it is not as sensitive to sward density, as outlined by Shalloo et al. [24]. Figure 5. (a) Schematic of ‘on the go’ grass measurement system presented in Fricke et al., reprinted with permission from ref. [95]. Copyright 2021 Elsevierand (b) image of similar system reprinted from ref. [96]. Figure 6. Elevation and cross section schematic of the C-DAX Pasturemeter. Terrestrial sensing of pasture may enable grass measurement to be conducted by autonomous ground vehicles (AGV), which work within close proximity to the ground in a remote manner. Research into these vehicles for PA applications has predominantly been focused on the arable sector. A more recent novel modiﬁcation of the C-DAX is a proposed pasture robot currently under development in New Zealand [105]. The concept combines an AGV with the C-DAX system. The robot is designed to autonomously navigate from a central charging station to a paddock and traverse the pasture using a pre-programmed sampling strategy, negating the need for physical labour. The entire area of a 2-ha paddock could be sampled for ﬁeld mapping purposes within 5 h, or a representative area of the same paddock could be sampled for basic grassland management purposes in under 30 min. Potential for ﬁtting soil sampling and grass quality sensors to this system is also being considered. Gobor et al. [106] proposed a similar pasture robot system for use on Agriculture 2021, 11, 600 13 of 38 German pastures. Their concept incorporates a mulcher system on the robotic platform (Figure 7) so that areas of rejected pasture, identiﬁed by a sward height sensor on the robot, can be mulched to encourage the regrowth of high-quality pasture. Likewise, areas of poor HM could be treated with a seeder incorporated on the proposed robotic rover platform. Sampling protocol design would need to be a signiﬁcant consideration with regard to the potential use of AGVs for pasture measurement. The design of optimum AGV sampling protocols for pasture measurement would need to be in line with best practice for pasture sampling. A signiﬁcant advantage of an AGV system would be that measurement labour and time do not place the same level of constraint on protocol design. Conversely, when compared with unmanned aerial vehicle (UAV)-based remote sensing, AGV systems have a number of disadvantages, including slower data collection, damage to sward caused by movement paths, and higher cost. Theses disadvantages may be offset by the higher resolution of measurement data and reduced climate noise interference that is achievable using AGVs when compared with remote sensing [107,108]. Figure 7. Pasture robot system concept proposed by Gobor et al. [108] incorporating mulcher and seeder. 8. Proximal Spectral Sensing In the context of this review, proximal spectral sensors refer to spectral sensors that operate within 2 m of the soil surface, as deﬁned by Viscarra Rossel et al. [109]. Proximal spectral sensing includes the previously discussed portable NIRS technologies, but the following section deals with all other prevalent proximal spectral sensing technologies. Hyperspectral sensing (HS) has the ability to capture a wide range of spectral data, ranging from the visible to NIR light regions, which results in greater availability of data for prediction modelling in comparison with NIRS. Devices for HS can be handheld for manual proximal sensing or mounted on un-manned aerial vehicles and satellites. Disadvantages of HS include the capture of a large amount of data that is redundant for modelling and the high cost of instrumentation [94]. Similar to NIRS, HS data can be used to model pasture quantity and quality using chemometric modelling techniques. Pullanagari et al. [110] used a HS canopy probe sensor (500–2400 nm) to predict a range of in-situ standing sward quality characteristics on PRG/WC dominant swards in New Zealand. The study achieved 2 2 satisfactory prediction results for CP (R = 0.78, RMSE = 2.33% DM), ME (R = 0.83, RMSE = 1 2 0.46 MJ kg ), and OMD (R = 0.83, RMSE = 4.02% DM). The samples used were not spread across an entire growing season and reference analysis was conducted by lab based NIRS. Hyperspectral sensing enables the prediction of sward characteristics by more basic means of spectral modelling referred to as vegetation indices (VI), which are commonly used for remote sensing applications. One of the most used VI is the normalised deference vegetation index (NDVI), which estimates the quantity of vegetation present by the ratio of red and NIR light wavelengths that are absorbed by pasture photosynthesis [24]. Another commonly researched VI is the leaf area index (LAI), which is a measure of the sward foliage area against ground area [94]. Reddersen et al. [94] found poor results for HS prediction Agriculture 2021, 11, 600 14 of 38 2 1 of HM using LAI (R = 0.36–0.44, SE = 1.5–1.8 t DM ha ) using the HS conﬁguration illustrated in Figure 8. The study further investigated the use of HS imagery (350–2500 nm) to predict HM by means of chemometric modelling with more positive results (R = 0.70– 0.89, SE = 0.66–0.85 t DM ha ). Moeckel et al. [97] discovered that normalized difference spectral index (NDSI) in combination with USH signiﬁcantly improved HM prediction 2 1 (R = 0.52, SE = 1000 kg DM ha ). Results for HS (305–1700 nm) prediction of HM were 2 1 poor (R = 0.48, SE = 950 kg DM ha ) and limitations in HS caused by the high presence of senescent material in the sward were observed later in the growing season. Ancin- Murguzur et al. [111] found a signiﬁcant correlation between HS and HM on Norwegian 2 2 mixed species swards (R > 0.55, RMSE 180 g m ), but noted increased error due to environmental inﬂuences on spectral signatures observed in cloudy and wet conditions. The study further showed that spectral data captured in the range of 350–900 nm was more robust against the inﬂuences of moisture. Pullanagari et al. [112] found strong correlations for CP (R = 0.65–0.83) on dairy pasture in New Zealand using HS. Askari et al. [113] found 2 1 2 positive results for predicting HM (R = 0.88, RMSE = 160 kg DM ha ) and CP (R = 0.82, RMSE = 10.0 g kg DM ) using a handheld HS camera on Irish PRG swards over two growing seasons. Figure 8. Schematic of hyperspectral sensing measurement system reprinted with permission from ref. [94].Copyright 2021 Elsevier. There are evident advantages to HS including non-destructive sampling, large sample area coverage, spatial variation identiﬁcation and potential incorporation with autonomous vehicles or tractor mounts. One of the main barriers to this technology is the high cost of HS devices, although this may decrease in the near future. Furthermore, HS and all other proximal spectral sensing technologies also have sampling issues with regard to accounting for spatial heterogeneity within swards. Agriculture 2021, 11, 600 15 of 38 9. Remote Sensing Remote sensing refers to all sensing techniques that operate at a distance greater than two meters from ground level [109]. This includes sensing methodologies that use UAVs, manned aircraft, and satellites. In the past decade, research on remote sensing methods for predicting grass yield and quality has increased. Remote sensing has the potential to cover larger sampling areas with minimal labour requirements. A range of remote sensing technologies can be ﬁxed to UAVs, which can ﬂy at low altitudes to obtain spectral data at high resolutions. Rueda-Ayala et al. [108] found weak correlations (R < 0.6) between red, green, blue (RGB) wavelength sensing data and HM on PRG dominant Norwegian swards and reported difﬁculties in measurement precision due to environmental factors such as wind speed, sunlight and cloud cover. Conversely, that study found that UAV sensing was less variable than terrestrial sensing data. Askari et al. [113] determined that red and green wavelength bands were important for predicting CP by means of UAV sensing on Irish PRG swards. Capolupo et al. [114] showed that UAV HS could predict sward height (R = 2 1 0.70–0.86, RMSE = 2.13–2.29 cm), HM (R = 0.36–0.83, RMSE = 2.95–3.81 kg DM plot ), 2 1 and CP (R = 0.56–0.76, RMSE = 11.73–12.28 g kg DM) on German controlled trial plots. Multi-spectral (MS) sensors that emit light radiation in discrete spectral bands and at broader resolutions than HS have been more commonly deployed in UAV research for pasture analysis. One major advantage of MS devices is that they are typically cheaper than HS instruments. Pullanagari et al. [115] reported reasonable precision (R = 0.6, 0.66, 0.68; RMSE = 2.88%, 065%, 5.27%) for parameters CP, ME, and OMD on New Zealand PRG dominant pastures over two grazing seasons using a proximal MS sensor, spanning 16 discrete wavelengths (460–1680 nm). A prominent issue with MS sensing was further highlighted in the study. Many MS sensors depend on natural light to illuminate the sward. Consequently, low atmospheric light intensity can cause sampling problems. Askari 2 1 et al. [113] reported good prediction results for HM (R = 0.78, RMSE = 215 kg DM ha ) 2 1 and CP (R = 0.77, RMSE = 13.6 g kg DM ) using UAV MS (Figure 9) on Irish PRG pastures over two grazing seasons. Togeiro de Alckmin et al. [58] reported that MS (R = 0.79, RMSE 1 1 = 405.8 kg DM ha ) had a 116 kg DM ha lower RMSE compared with the RPM for HM prediction, when an optimal selection of VI was used. Oliveira et al. [116] showed that a combination of HS sensing and 3D imagery out-performed MS measurements on Finish swards, accurately predicting silage sward HM (RPE = 14.6%), digestibility (RPE = 1.9%), and N content (RPE = 13.6%). A number of similar limitations have been reported for both proximal and aerial spec- tral sensing of pasture. The most signiﬁcant limitation is the heterogeneity of grassland, which is much greater than tillage, where remote spectral sensing has become more estab- lished. The temporal change in the ratio of photosynthetically to non-photosynthetically active (vegetative vs. dead) material in grassland swards has signiﬁcant effects on spec- tral absorption. Achieving adequate levels of spatial resolution to distinguish signiﬁcant variations in pasture performance for targeted management purposes is also an issue with pasture sensing. Sensors with sufﬁcient spatial and sensing resolution to identify pasture variation can be very expensive. Similar to NIRS, high moisture content within standing swards can obscure spectral features of certain quality parameters [112]. Light detection and ranging (LiDAR) is another potential technology that could be used in conjunction with UAVs for remote sensing of pasture. This technology utilises light beams (visible/infrared) emitted at a high irradiance rate to measure the distance and shape of terrestrial objects. The time it takes for each emitted light beam to be reﬂected back to the LiDAR sensor receiver is used to develop a point cloud dataset for each target object. Obanawa et al. [117] reported an average absolute error of 12 mm (10 mm) (R = 0.93) at a 20 mm resolution for LiDAR prediction of grass height on Italian ryegrass pasture in Japan. Disadvantages of LiDAR include its relatively high cost and susceptibility to high measurement error in windy conditions [118,119]. Moreover, the use of grass height to predict HM has further limitations as previously discussed. Agriculture 2021, 11, 600 16 of 38 Figure 9. (a) UAV with multispectral sensor and (b) UAV plot sensing ﬂy over from study by Askari et al. [113]. Several studies have investigated the potential of utilising satellite-based MS and HS to predict pasture quantity and quality [111,113,120]. The distinct advantages of satellite sens- ing relate to the larger spatial coverage, in terms of data acquisition, that can be achieved. The European Space Agency’s Sentinel-2 project comprises of two orbital satellites loaded with MS technology capable of monitoring land use variations at 10 m, 20 m, and 60 m resolutions [121]. Sibanda et al. [120] outlined how Sentinel-2 MS data could be used to predict HM with comparable accuracy to proximal HS on South African experimental 2 2 1 grassland plots (27–250 m )(R = 0.58, RMSE = 67.9 kg ha ). Askari et al. [113] reported 2 1 moderate success for predicting HM (R = 0.82, RMSE = 600 kg DM ha ) and poor results 2 1 for CP (R = 0.62, RMSE = 13.3 g kg DM) using Sentinel-2 data on Irish grassland plots (7.5 m ) and grazed paddocks ( 1 ha). The study illustrated that the overriding limitation for satellite spectral sensing on Irish pasture is frequent cloud cover, as data acquisition was not possible on days with over 30% cloud cover. An alternative technology for satellite remote sensing of pasture that may overcome cloud cover and illumination limitations is synthetic aperture radar (SAR), which uses high resolution radio wave reﬂectance to predict pasture height. Barrett et al. [122] utilized SAR to overcome cloud cover limitations for satellite classiﬁcation of Irish grasslands. A more recent study that used SAR on Irish PRG dominant dairy pasture (1 ha) yielded 2 2 promising results for both sward height (R = 0.55) and HM (R = 0.75) [123] at a 25 cm spatial resolution. However, research into this technology is still at an early stage. In light of the research outlined for terrestrial, proximal, and aerial sensing techniques, it is evident that longer, more detailed studies over numerous seasons and sward types need to be conducted before these technologies can become established within pasture-based agriculture. Results from the most recent research ﬁndings discussed in this review, which were most relevant to the measurement of temperate grasslands used for pasture-based livestock production (PRG/WC Irish pasture), are summarised in Table 3. Agriculture 2021, 11, 600 17 of 38 Table 3. Summary of grass measurement systems from the research discussed in this review that were most relevant to temperate (Irish) grasslands. Relevant Sample System Region Measure Prediction Herbage Quantity Herbage Quality Advantage Disadvantage Studies No. Conventional systems Error (g kg , g Error (kg DM 2 2 1 d e R R kg DM , % , 1 a b ha , mm ) % DM ) Murphy et al. Compressed Rapid, usability, Labour intensive, a , Rising plate meter Ireland HM 1977 0.77 354 * - - [59] sward height cost accuracy O’ Donovan et al. Perceived a, I Visual assessment Ireland HM 2205 0.95 193 - - Minimal labour High subjectivity [26] herbage cover Murphy et al. Spectral High cost, lab c d, I NIRS Ireland DM, CP 1812 - - 0.86, 0.84 9.46 , 20.38 Accuracy [84] absorption based, destructive State of the art Light sensing Sward surface a, I Schori [103] Switzerland HM 439 0.77 311 - - Rapid, automation Accuracy (C-DAX) height Obanawa et al. Sward surface Sward surface High cost, wind b, LiDAR Japan 25 0.93 - - Remote sensing 12 ** [117] height height error, accuracy Reddersen et al. Sward surface a, Ultrasonic Germany HM 167 0.76 880 * - - Rapid, automation Accuracy [94] height Victoria, Spectral DM, DMD, WSC 0.69, 3.14 , 2.70, 2.77, In-situ quality Portable NIRS Smith et al. [89] Aus- 540 - - Accuracy f, absorption CP 0.82,0.49,0.74 2.02 * analysis tralia Hyperspectral Askari et al. Spectral Remote sensing, a, d, Ireland HM, CP 84 0.88 160 * 0.82 10 * High cost sensing [113] absorption accuracy Multispectral Askari et al. Spectral Remote sensing, Lack of long term a, d, Ireland HM, CP 126 0.78 215 * 0.77 13.6 * sensing [113] absorption cost studies Satellite Askari et al. Spectral Cloud cover, a, d, Ireland HM, CP 176 0.82 600 * 0.62 Remote sensing 13.3 * multispectral [113] absorption accuracy Synthetic Aperture Sward surface Ali et al. [123] Ireland HM 264 0.75 - - - Satellite sensing Lack of research radar height 1 a 1 1 c e 1 d f Measure = measurement parameter, Prediction = prediction parameter; HM = herbage mass (kg DM ha ); DM = dry matter (g kg ha , % ); CP = crude protein (g kg DM , %DM ); DMD = dry matter f f 2 I digestibility (%DM ); WSC = water soluble carbohydrates (%DM ); R = coefﬁcient of determination; Error = * RMSE, standard error, ** mean absolute error depending on study; ‘-‘ = denotes where data was not published as part of study. Agriculture 2021, 11, 600 18 of 38 10. Decision Support Systems for Grassland Measurement Decision support tools (DST) are becoming more frequently used by grassland farmers to optimise the end use of their grass measurement data for the purposes of herbage allocation and pasture management. A number of grassland management DSTs have been developed in Europe [124,125] and an increasing amount of grassland data is being stored on cloud computing platforms. PastureBase Ireland (PBI) is a DST that assists farmers in determining appropriate actions to be taken to optimise grassland management, mainly by processing uploaded pasture HM cover estimations to determine appropriate herbage allocations in accordance with on-farm growth rates [20]. One signiﬁcant advantage of DSTs, such as PBI, is that they can perform as national databases for research and innovation. PastureBase can capture data for a range of paddock management parameters from farms across Ireland, which can be used for regional research studies [126]. User collaboration by means of online discussion group portals is also enabled through PBI’s interface [24]. Recent data from PBI indicates that farmers using the system are utilising more grass than the Irish national average (8 t DM year ) and are growing between 11 and 15 t DM year [127]. Studies have utilised online DST databases to combine grassland management factors with measurement and meteorological data from local weather stations to forecast HM growth rates [128,129]. Romera et al. [130] utilised an algorithm to continuously train a model to simulate growth factors between measurement dates on New Zealand dairy pastures. These growth factor simulations were based on a combination of meteorological and grass measurement data. Herrmann et al. [131] combined N fertilization, defoliation frequency, grass species, and daily weather data to predict HM and CP on pastures in Germany. In the near future, on-farm sensor technologies could provide data on site-speciﬁc meteorological and soil conditions to increase HM prediction accuracy [69]. One limitation of the previously mentioned DSTs is that they are currently only capable of processing HM and sward height data, which are acquired using conventional measurement techniques. Scope for a holistic grass management DST that incorporates state of the art grass technologies, which can measure both pasture quantity and quality, has been identiﬁed [132]. GrassQ was a European wide project that aimed to develop a holistic precision grassland measurement and management system, which encompassed both ground based and remote sensing measurement technologies [133]. For new DSTs to be adopted for regular use by grassland farmers, they will need to ensure reduction in labour and return of investment. The GMOT, a prototype grass measurement optimisation tool developed by Murphy et al. [35], generated grass measurement protocols that were optimised for both precision and labour efﬁciency. The tool was capable of optimising measurement routes and simulating measurement error, which facilitated cost beneﬁt analysis to be conducted for each measurement protocol based on measured HM vs. estimated labour and error costs. Cost–beneﬁt analysis should be an integral part of the design of any future grass management support system to determine the efﬁcacy of investing in new measurement technologies at farm level [24]. 11. Current Challenges Relating to Precision Pasture Measurement Signiﬁcant challenges currently restricting the implementation of precision pasture measurement at farm level that have been highlighted in the reviewed literature include sward heterogeneity, labour, and perceived measurement value amongst farmers. The lack of validation, robustness, and high cost of state-of-the-art measurement technolo- gies are further challenges to the optimisation of pasture measurement. The high spatial and temporal variability of grazed pasture has represented a signiﬁcant hindrance to the precision of conventional grass measurement technologies. One perceived solution to over- come poor measurement precision relating to highly variable swards has been to increase measurement sampling rates and ultimately measurement labour. Measurement errors caused by sward heterogeneity, high labour cost, and the poor precision of conventional grass measurement methods have resulted in poor perceptions and low uptakes in grass Agriculture 2021, 11, 600 19 of 38 measurement amongst farmers. Some of the state-of-the-art technologies discussed in this paper have the potential to overcome these issues. However, a period of time is required for long term studies that have performed sufﬁcient validation of the proposed technologies to become established in the literature. A number of studies outlined in this review have indicated the detrimental effects that climate conditions, such as excessive cloud, wind, and rain, have on pasture sensing data. Additionally, the potential high cost of new grass measurement sensors will not alleviate the poor perception that some farmers have of the value of frequent grass measurement. 12. Future of Grassland Measurement Within the literature outlined in this review, it is evident that there is considerable scope for the development of grassland sensing techniques to increase measurement precision, pasture mapping capabilities, and labour efﬁciency. Considerable potential exists to develop holistic grass measurement systems including multi-sensor conﬁgurations, which incorporate the beneﬁts of a range of measurement technologies. Concurrently, the combination of new grassland sensing technologies with state-of-the-art modelling techniques should lead to more precise predictions of pasture parameters. This will enable the exploitation of a wide range of data sources, including measurement, management, and climate factors, which would be facilitated by online DSTs. Moreover, analysis of mixed species swards should be accounted for within the design and calibration of future grass measurement technologies. Regarding the new technologies discussed in this review, more detailed long-term studies that account for annual and seasonal sward variation are required. Furthermore, scope exists to automate grass measurement using either manned or unmanned vehicles and this would aid the promotion of precision grass measurement amongst farmers. More research is required regarding the optimisation of grass mea- surement protocols that account for spatial and temporal heterogeneity in pasture in line with the principles of PA. The development of such protocols should be applicable to both herbage quantity and quality measurement techniques. The adoption of new preci- sion grassland measurement technologies within pasture-based industries will only be justiﬁed if these technologies are proven to be signiﬁcantly more precise and practical than established methods. Detailed cost–beneﬁt analysis will be required to justify the implementation of new measurement technologies at farm level. Additionally, new mea- surement technologies will need to have minimal labour requirements, be easy to use, and adequate training will need to be provided to farmers to promote frequent measurement of pasture. This will further ensure that high resolution and accurate grassland data are regularly recorded. 13. Conclusions This review summarised the basic principles of optimal grassland management on temperate pastures and the requirement for more precise and efﬁcient measurement tech- nologies in line with the concept of PA. The development of more robust and rapid technologies to predict pasture quantity and quality would enable the optimisation of herbage allocation and utilisation. Subsequently, this would lead to increases in proﬁtabil- ity and reductions in emissions within pasture-based systems. The main ﬁndings from this review were: The dominant factors that need to be addressed with regard to the development of pre- cision grassland measurement technologies are sward heterogeneity and measurement labour and cost There are no established technologies for determining real-time in-situ pasture qual- ity. The development of such technologies is vital for a more precise management of pasture. The development and integration of holistic grassland management and measurement systems is necessary to achieve precision grassland management. Agriculture 2021, 11, 600 20 of 38 Author Contributions: Conceptualization, D.J.M., M.D.M., B.O., M.O.; methodology, D.J.M., M.D.M., B.O., M.O.; software, D.J.M.; validation, M.D.M., B.O. and M.O.; formal analysis, D.J.M.; investigation, D.J.M.; resources, M.D.M., B.O., M.O.; data curation, D.J.M.; writing—original draft preparation, D.J.M.; writing—review and editing, B.O., M.D.M., M.O.D.; visualization, D.J.M.; supervision, D.J.M.; project administration, M.D.M., B.O.; funding acquisition, M.D.M., B.O. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the the ICT–AGRI GrassQ project (grant number 35779) and the Irish Department of Agriculture, Food and the Marine and the European Commission’s ERA-NET, ICT–AGRI scheme as part of the Horizon 2020 programme. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: This research was supported by the the ICT–AGRI GrassQ project (grant number 35779) and the Irish Department of Agriculture, Food and the Marine and the European Commission’s ERA-NET, ICT–AGRI scheme as part of the Horizon 2020 programme. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A Table A1. Summary of literature review dataset of studies relevant to grass measurement on temperate (Irish) grassland. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Grassland sward heterogeneity Sampling strategies for mapping “within-ﬁeld” Develop a variability in the Jordan protocol to Herbage 2003 dry matter yield Ireland PRG 1 Paddock et al. [34] measure and cuts and mineral map DM nutrient status of forage grass crops in cool temperate climes Correcting fresh grass allowance for Measure the rejected patches Klootwijk extent of rejected The Nether- 2019 due to excreta in PRG RPM 2 Paddock et al. [9] patches within lands intensive grazing pasture systems for dairy cows Frequency Measure the distributions of range and Barthram 2005 sward height distribution of Scotland PRG/mixed Sward stick 2 Paddock et al. [8] under sheep grass height grazing within pasture Variation in composition of Measure the Wilkinson pre-grazed pasture variation of grass 2014 UK Mixed NIRS 7 Paddock et al. [23] herbage in the quality in UK United Kingdom, pasture 2006–2012 Conventional grass measurement systems Agriculture 2021, 11, 600 21 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Critical analysis Herbage Techniques for of conventional cuts, RPM, Cayley & 1996 measuring pasture Australia - capacitance - Paddock Bird [43] pastures measurement meter, techniques sward stick The effect of intensive grazing Investigate the systems on the Klootwijk effect of grazing The Nether- 2019 rising plate meter PRG RPM 2 Paddock et al. [28] systems on RPM lands calibration for calibration perennial ryegrass pastures A comparison of Comparison of Visual methods used to conventional Nova Martin estimation, 2005 determine biomass pasture Scotia, Mixed 1 Paddock et al. [42] sward stick, on naturalized measurement Canada RPM swards methods Visual Comparison of Measuring estimation, conventional Mannetje biomass of The Nether- sward stick, 2000 pasture - - Paddock [44] grassland lands RPM, measurement vegetation remote methods sensing Factors inﬂuencing the accuracy of Calibration of Thomson herbage mass New Capacitance 1983 capacitance Mixed 2 Paddock [45] determinations Zealand meter meter with a capacitance meter Evaluation and calibration of an Earle & automated rising Mc Calibration of Victoria, 1979 plate meter for PRG RPM 2 Paddock Gowan RPM Australia estimating dry [46] matter yield of pasture Seasonal variation Ferraro in the rising plate Calibration of 2002 Ohio, USA Mixed RPM 3 Paddock et al. [47] meter calibration RPM for forage mass O’ Calibration of Visual assessment Visual Donovan 2002 visual Ireland PRG 2 Paddock of herbage mass assessment et al. [48] assessment Visual Comparison of A comparison of estimation, O’ conventional four methods of sward stick, Donovan 2002 pasture Ireland PRG 2 Paddock herbage mass RPM, et al. [26] measurement estimation capacitance methods meter The visual Calibration of Campbell Western, Visual 1973 assessment of visual Mixed 1 Paddock [49] Australia assessment pasture yield assessment Agriculture 2021, 11, 600 22 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Evaluation of Assessment of techniques for double sampling estimating the herbage technique Stockdale yield of irrigated Victoria, cuts and 1984 involving PRG/WC/mixed 1 Paddock [50] pastures Australia visual herbage cuts and intensively grazed assessment visual by dairy cows 1. assessment Visual assessment Visual Estimation of Comparison of L’Huillier estimation, herbage mass in conventional & New sward stick, 1988 ryegrass/white pasture PRG/WC 2 Paddock Thomson Zealand RPM, clover dairy measurement [51] capacitance pastures methods meter Investigate causes of Estimation of dairy Visual Thomson variation in New 1997 pastures-the need PRG/WC assessment, 2 Paddock et al. [52] pasture Zealand for standardisation RPM measurement across regions Practical use of the Assess the rising plate meter Visual Lile et al. measurement New 2001 (RPM) on New PRG/WC assessment, 3 Paddock [53] precision of the Zealand Zealand dairy RPM RPM farms Development of The Value of a double O’ Pasture Height in sampling Herbage Sullivan 1987 the Measurement Ireland PRG 1 Paddock technique for cuts, RPM et al. [54] of Dry Matter measuring Yield pasture Micro-sonic sensor technology enables Development of McSweeney enhanced grass GPS enabled 2019 Ireland - RPM 1 - et al. [55] height rising plate measurement by a meter Rising Plate Meter Greater understanding the density of grass to calculate the Calibration of Defrance 2004 growth and rising plate France PRG/WC RPM 13 Paddock et al. [56] biomass of a plot meter and the stock of grass available on a farm Comparison of Calibration of ﬁve Holshof different rising The Nether- 2015 rising plate meters PRG RPM 1 Plots et al. [57] plate meter lands in the Netherlands models Estimating forage Comparison of mass with a Sward stick, conventional Sanderson commercial Eastern, RPM, 2001 pasture Mixed 2 Paddock et al. [27] capacitance meter, USA capacitance measurement rising plate meter meter methods and pasture ruler Agriculture 2021, 11, 600 23 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons A survey analysis of grassland dairy farming in Ireland, Investigate investigating grassland Creighton 2011 grassland management Ireland PRG - 1 Paddock et al. [21] management, practices in technology Ireland adoption and sward renewal Utilising grassland management and Calibration of climate data for rising plate Murphy more accurate meter using state 2021 Ireland PRG RPM 3 Paddock/Plots et al. [59] prediction of of the art herbage mass modelling using the rising techniques plate meter Review of advancement of Herbage Mannetje Advances in grassland science The Nether- cuts, 2002 - - Paddock [60] grassland science and lands remote measurement sensing techniques Regular estimates of herbage mass Investigate the Beukes can improve effect of grass New 2019 PRG - 1 Paddock et al. [22] proﬁtability of measurement on Zealand pasture-based farm proﬁtability dairy systems Pasture sampling techniques Evaluation of the precision of the Assessment of rising plate meter RPM Murphy for measuring measurement 2020 Ireland PRG/WC RPM 2 Paddock/Plot et al. [29] compressed sward precision and height on sampling heterogeneous protocol grassland swards A method for approximate Development of Visual on-farm estimation a double Nakagami assessment, 2016 of herbage mass by measurement Japan Mixed 1 Paddock [10] herbage using two method for cuts, RPM assessments per pasture pasture Understanding Tasmanian dairy Investigate farmer adoption of farmer behaviour pasture Hall et al. with regard the 2019 management Tasmania - - 1 Paddock [64] adoption of grass practices: A measurement Theory of Planned technology Behaviour approach Developing an approach to assess Identify Eastwood farmer perceptions perceived value New 2020 - - 1 Paddock et al. [65] of the value of of grass Zealand pasture assessment measurement technologies Agriculture 2021, 11, 600 24 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons A protocol for Develop a grass Hutchinson New RPM, 2016 sampling pastures measurement Mixed 3 Paddock [66] Zealand C-DAX in hill country protocol Spatial variability Map and of soil properties Bernardi evaluate the Herbage 2016 and yield of a Brazil Alfalfa 1 Paddock et al. [68] spatial variation cuts grazed alfalfa of forage yield pasture in Brazil The role of precision agriculture in Review of the optimising soil potential for Higgins nutrient status and precision & Bailey 2017 grassland Ireland - - - Paddock agriculture in [69] productivity in grassland Northern Ireland, agriculture while reducing nutrient losses to air or water Measuring labour Quantiﬁcation of Herbage input on Deming labour input for cuts, RPM, 2018 pasture-based Ireland PRG 1 Paddock et al. [63] speciﬁc tasks on visual dairy farms using Irish dairy farms assessment a smartphone State of the art grass measurement systems Comparing methods to Togeiro estimate perennial Comparison of de RPM, hy- 2020 ryegrass biomass: RPM and remote Tasmania PRG 1 Plot Alckmin perspectral canopy height and sensing et al. [58] spectral vegetation indices Development of a grass Development of measurement a decision Murphy optimisation tool 2020 support tool to Ireland PRG RPM 3 Paddock/Plot et al. [35] to efﬁciently optimise grass measure herbage measurement mass on grazed pastures Practical Review of the Posudin spectroscopy in fundamentals of 2007 USA - NIRS - - [70] agriculture and agri- food science spectroscopy The use of NIRS to predict the Development of chemical de Boever NIRS for 1995 composition and Belgium - NIRS - - et al. [72] concentrate feed the energy value of quality analysis compound feeds for cattle Predicting Forage Development of Quality by NIRS for dried Norris 1976 Infrared and milled USA - NIRS - - et al. [73] Reﬂectance forage quality Spectroscopy analysis Agriculture 2021, 11, 600 25 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Predicting the dry matter intake of grazing dairy cows Development of Lahart 2019 using infrared NIRS to predict Ireland PRG/WC NIRS 3 Paddock et al. [74] reﬂectance dry matter intake spectroscopy analysis A Note on Development Estimation of NIRS Jafari Quality calibrations to 2003 Ireland PRG NIRS 2 Paddock/Plot et al. [75] Parameters in predict quality of Perennial Ryegrass dried and milled by near Infrared grass A note on the comparison of Development three near infrared NIRS PRG, reﬂectance Burns calibrations to Italian & 2014 spectroscopy Ireland NIRS 2 Plot et al. [76] predict quality of hybrid calibration dried and milled grass strategies for grass assessing herbage quality of ryegrass Assessment of Development herbage yield and NIRS quality traits of Burns calibrations to 2013 perennial Ireland PRG NIRS 3 Plot et al. [30] predict quality of ryegrasses from a dried and milled national variety grass evaluation scheme Effect of Development preparation NIRS Alomar method on calibrations to 2003 Chile Mixed NIRS 1 Paddock et al. [77] composition and predict quality of NIR spectra of dried and milled forage samples grass Near infrared technology for precision environmental Potential of NIRS McClure measurements: 2002 to analysis fresh Australia Fescue NIRS 1 Plot et al. [78] Part 1. grass N content Determination of nitrogen in green- and dry-grass tissue Effects of sample preparation and measurement standardization on Reddersen the NIRS Development in calibration quality NIRS to analysis Wachen- 2013 Germany Mixed NIRS 2 Plot of nitrogen, ash standing sward dorf and NDFom quality [79] content in extensive experimental grassland biomass Agriculture 2021, 11, 600 26 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Assessing the accuracy of current near infra-red reﬂectance Development of spectroscopy Thomson NIRS for 2018 analysis for fresh UK Mixed/WC NIRS 3 Paddock et al. [80] grass-clover grass-clover silage analysis mixture silages and development of new equations for this purpose Prediction of the composition of fresh pastures by Development of Alomar near infrared NIRS to analysis 2009 Chile Mixed NIRS 1 Paddock et al. [81] reﬂectance or fresh grass interactance- quality reﬂectance spectroscopy Impact of sampling and storage technique, Use of fresh and duration of grass NIRS to storage, on the analysis the Dale et al. 2017 composition of impact of sample Ireland PRG NIRS 1 Plot [82] fresh grass when storage and analysed using preparation near-infrared techniques reﬂectance spectroscopy Calibration models for the nutritional Development of quality of fresh Lobos NIRS to analysis 2019 pastures by Chile Mixed NIRS 2 Paddock et al. [83] fresh grass near-infrared quality reﬂectance spectroscopy A near infrared spectroscopy Development of Murphy calibration for the NIRS to analysis 2021 Ireland PRG NIRS 3 Paddock/Plot et al. [84] prediction of fresh fresh grass grass quality on quality Irish pastures Prediction performances of Evaluation of portable near Berzaghi maize silage Portable 2005 infrared Italy Maize 3 Paddock et al. [85] quality with NIRS instruments for at portable NIRS farm forage analysis A review on the applications of portable Review of the Teixeira 2013 near-infrared use of NIRS in Portugal - NIRS - - et al. [86] spectrometers in Agriculture the agro-food industry Agriculture 2021, 11, 600 27 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons NIR-Spectroscopy of non-dried forages as a tool in Development of Feuerstein breeding for higher portable NIRS to Portable & Paul 2007 Germany Mixed 6 Plot quality–laboratory analysis fresh NIRS [87] tests and online grass quality investigations on plot harvesters Use of portable NIRS equipment in Development of Mendarte ﬁeld conditions to portable NIRS to Basque Portable 2010 Mixed 1 Paddock et al. [88] determine the analysis fresh Country NIRS nutritional value of grass quality mountain pastures Machine learning algorithms to predict forage nutritive value of Development of Smith in situ perennial hyperspectral Victoria, 2020 PRG Hyperspectral 1 Plot et al. [89] ryegrass plants sensing for grass Australia using quality analysis hyperspectral canopy reﬂectance data The Use of Mobile Development of Near-Infrared Bell et al. portable NIRS to Portable 2018 Spectroscopy for UK Mixed/PRG/WC 1 Paddock [90] analysis fresh NIRS Real-Time Pasture grass quality Management Portable NIRS: a Assessment of novel technology Patton portable NIRS Portable 2018 for the prediction Ireland PRG 1 Paddock et al. [91] for fresh grass NIRS of forage nutritive quality analysis quality Comparison of Spectral Reﬂectance-Based Comparison of Smart Farming remote sensing Tools and a Portable Hart et al. and conventional 2020 Conventional Switzerland Mixed NIRS, Mul- 1 Plot [92] grass Approach to tispectral measurement Determine technologies Herbage Mass and Grass Quality on Farm Evaluating Use of Soil-Borne Causes multispectral of Biomass UAV and Multispectral, Vogel 2019 Variability in proximal sensing Germany Mixed proximal 1 Paddock et al. [93] Grassland by to evaluate sensing Remote and biomass Proximal Sensing variability The use of A multi-sensor hyperspectral approach for sensing and Ultrasound, Reddersen predicting biomass 2014 ultrasound to Germany Mixed Hyperspec- 2 Plot et al. [94] of extensively predict grass tral managed quality and grassland quantity Agriculture 2021, 11, 600 28 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Comparing mobile and static The use of a assessment of mobile Ultrasound, Safari biomass in muti-sensor unit 2016 Germany Mixed Hyperspec- 2 Paddock et al. [96] heterogeneous to measure grass tral grassland with a quantity and multi-sensor quality system Fusion of Ultrasonic and The use of Spectral Sensor hyperspectral Data for sensing and Ultrasound, Moeckel Improving the 2017 ultrasound to Germany Mixed Hyperspec- 1 Paddock et al. [97] Estimation of predict grass tral Biomass in quality and Grasslands with quantity Heterogeneous Sward Structure Development of Legg & Ultrasonic ultrasonic New Bradley 2019 Proximal Sensing sensors for rapid PRG Ultrasound 1 Plot Zealand [98] of Pasture Biomass measurement of grass height Calibration of the C-DAX Rapid Pasturemeter and Calibration of Rennie the rising plate the C-DAX to New 2009 PRG/WC C-DAX 1 Paddock et al. [99] meter for measure grass Zealand kikuyu-based quantity Northland dairy pastures Review of precision Lawrence Pasture agriculture tools New C-DAX, et al. 2007 Monitoring - - Paddock for pasture Zealand NIRS [100] Technologies measurement and mapping Pasture Mass Comparison of Estimation by the RPM, C-AX and King et al. C-DAX Pasture New 2010 herbage cuts for PRG/WC/Mixed C-DAX 1 Paddock [101] Meter: Regional Zealand grass Calibrations for measurement New Zealand Calibration of the Calibration of Oudshoorn C-DAX pasture C-DAX for grass et al. 2011 Denmark PRG/WC C-DAX 2 Plot meter in a Danish quantity [102] grazing system measurement Sward surface height estimation Comparison of Schori with a rising plate RPM and C-DAX et al. 2015 Switzerland Mixed C-DAX 4 Paddock meter and the for grass [103] C-Dax measurement Pasturemeter Development of Dennis Pasture yield measurement New et al. 2015 mapping: why & protocol for the - C-DAX 2 Paddock Zealand [104] how C-DAX to map pasture yield Agriculture 2021, 11, 600 29 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Introducing the Agri-Rover: An Manderson Automation of Autonomous New & Hunt 2013 C-DAX using - C-DAX - Paddock on-the-go sensing Zealand [105] robotics rover for science and farming Advanced pasture Development of Gobor management pasture care and et al. 2015 through innovative Germany Mixed Laser, NIRS 1 Paddock management [106] robotic pasture robots maintenance Comparison of Urban Lawn Marin remote and Monitoring in RGB et al. 2018 ground Spain - 1 Plot Smart City sensing [107] automated grass Environments measurement Proximal Soil Viscarra Sensing: An Calibration of Rossel Effective Approach proximal sensing France, 2011 - NIRS - - et al. for Soil techniques for Australia [109] Measurements in soil analysis Space and Time In-ﬁeld Calibration of hyperspectral Pullanagari hyperspectral proximal sensing New et al. 2012 sensing for grass Mixed Hyperspectral 1 Paddock for estimating Zealand [110] quality quality parameters measurement of mixed pasture Yield Estimates by Calibration of a Two-Step Ancin- proximal and Approach Using Murguzur satellite 2019 Hyperspectral Norway Mixed Hyperspectral 4 Paddock et al. hyperspectral Methods in [111] sensing for grass Grasslands at High measurement Latitudes Investigation of Pasture quality Pullanagari optical sensor for Multispectral, measurement tools New et al. 2011 the measurement - Hyperspec- 1 Paddock for decision Zealand [112] of pasture tral making quality Evaluation of Calibration of Grass Quality proximal and Askari under Different remote sensing Multispectral, et al. 2019 Soil Management methods for Ireland PRG/WC Hyperspec- 2 Paddock [113] Scenarios Using pasture quantity tral Remote Sensing and quality Techniques measurement Comparing UAV-Based Technologies and Evaluation of Rueda- RGB-D aerial and Ayala Reconstruction ground based RGB-Depth 2019 Norway Mixed 1 Paddock et al. Methods for Plant method for grass sensor [108] Height and quantity Biomass measurement Monitoring on Grass Ley Agriculture 2021, 11, 600 30 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Estimating Plant Traits of Statistical Grasslands from modelling Capolupo UAV-Acquired methods for et al. 2015 Hyperspectral hyperspectral Germany - Hyperspectral 1 Plot [114] Images: A grass Comparison of measurement Statistical data Approaches Proximal sensing of the seasonal Measuring the Pullanagari variability of variability of New et al. 2012 pasture nutritive pasture quality PRG/WC Multispectral 1 Paddock Zealand [115] value using using proximal multispectral sensing radiometry Machine learning estimators for the quantity and quality of grass Utilisation of Oliveira swards used for UAV sensing to RGB, Hy- et al. 2020 Finland Mixed 1 Paddock silage production measure silage perspectral [116] using drone-based grass quality imaging spectrometry and photogrammetry Portable LiDAR-Based Method for Development of Obanawa Improvement of LiDAR to Italian et al. 2020 Grass Height Japan LiDAR 1 Plot measure grass ryegrass [117] Measurement height Accuracy: Comparison with SfM Methods Review of 3D 3-D Imaging Vázquez- image Systems for 3-D Arellano technology for 2016 Agricultural Germany - imaging - Paddock et al. precision Applications—A systems [118] agriculture Review applications Examination of the potential of terrestrial laser scanning and Comparison of Cooper structure-from- LiDAR and RPM South Smooth LiDAR, et al. 2017 motion for grass Dakota, 1 Plot Brome RPM [119] photogrammetry quantity USA for rapid measurement non-destructive ﬁeld measurement of grass biomass Comparing the spectral settings of the new generation Comparison of broad and narrow proximal and Sibanda band sensors in Multispectral, satellite sensing South et al. 2016 estimating biomass Mixed Hyperspec- 1 Plot for grass Africa [120] of native grasses tral quantity grown under measurement different management practices Agriculture 2021, 11, 600 31 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Assessment of multi-temporal, multi-sensor radar and ancillary Calibration of Barrett spatial data for satellite radar for Satellite et al. 2014 Ireland PRG/WC - Paddock grasslands grassland radar [122] monitoring in classiﬁcation Ireland using machine learning approaches Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Calibration of Ali et al. Coherence to satellite radar for Satellite 2017 Ireland PRG 1 Paddock [123] Monitor Pasture grass quantity radar Biophysical measurement Parameters: Limitations and Sensitivity Analysis Grass measurement decision support systems PastureBase Development of Ireland: A grassland Rising plate Hanrahan grassland decision management meter, 2017 Ireland PRG 2 Paddock/Plot et al. [20] support system decision support Visual and national tool and national estimation database database Pastur ’Plan: a Introduction to a dynamic tool to decision support Delaby support grazing tool for et al. 2015 management France - RPM - Paddock grassland [124] decision making in measurement a rotational and management grazing system Development of GrazeVision: A Zom & a decision versatile grazing The Nether- Holshof 2011 support model - - - Paddock decision support lands [125] for grassland model management PastureBase Development of O’ Leary Ireland—getting grassland Rising plate & O’ Ireland utilising management meter, 2019 Ireland PRG - Paddock Donovan more grass. decision support Visual [127] Moorepark ’19 tool and national estimation Irish Dairy database The use of Weather forecasts McDonnell weather Grass to enhance an Irish et al. 2019 forecasting to Ireland PRG growth 4 Paddock grass growth [128] predict grass model model growth Agriculture 2021, 11, 600 32 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Development of the Moorepark St Gilles grass Development of Ruelle growth model Grass a grass growth et al. 2018 (MoSt GG model): Ireland PRG growth 2 Paddock model for Irish [129] A predictive model model pasture for grass growth for pasture based systems Use of a pasture growth model to Development of Romera estimate herbage Grass a grass growth New et al. 2010 mass at a paddock PRG growth 1 Paddock model for New Zealand [130] scale and assist model Zealand pasture management on dairy farms Performance of grassland under different cutting regimes as affected Calibration of Herrmann Grass by sward forage growth et al. 2005 Germany PRG/WC/mixed growth 3 Plot composition, and quality [131] model nitrogen input, soil model conditions and weather-A simulation study GrassQ-a holistic Development of precision grass decision support RPM, Hy- Murphy measurement and system to perspectral, et al. 2019 analysis system to process data Ireland PRG 2 Paddock multispec- [132] optimize pasture from multiple tral based livestock measurement production systems Modelling Development of precision grass decision support RPM, Hy- O’ Brien measurements for system to perspectral, et al. 2019 a web-based process data Ireland PRG 2 Paddock multispec- [133] decision platform from multiple tral to aid grassland measurement management systems DM = Dry matter, PRG = Perennial rye grass, WC = white clover, Paddock = predominately grazed pasture > 0.25 ha, Plots = simulated grazed plots <0.25 ha. 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ISSN: 2077-0472
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Abstract

agriculture Review A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland 1 , 2 2 1 1 , Darren J. Murphy , Michael D. Murphy , Bernadette O’Brien and Michael O’Donovan * Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, Ireland; darren.murphy@teagasc.ie (D.J.M.); bernadette.obrien@teagasc.ie (B.O.) Department of Process, Energy and Transport Engineering, Munster Technological University, Cork, Ireland; michaeld.murphy@cit.ie * Correspondence: michael.odonovan@teagasc.ie 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. Keywords: pasture-based agriculture; precision agriculture; remote sensing; spectroscopy; grass Citation: Murphy, D.J.; Murphy, measurement; grassland sampling M.D.; O’Brien, B.; O’Donovan, M. A Review of Precision Technologies for Optimising Pasture Measurement on Irish Grassland. Agriculture 2021, 11, 600. https://doi.org/10.3390/ 1. Introduction agriculture11070600 Demand for animal protein products is predicted to increase by >70% in the coming decades as a consequence of the growing distribution of wealth in developing coun- Academic Editor: Dionissios Kalivas tries [1,2]. Consequently, this could potentially result in an 80% increase in agricultural GHG emissions, which would critically impact the environment if not mitigated [3]. Grass- Received: 7 June 2021 land based agriculture has a signiﬁcant role to play in terms of increasing food production Accepted: 18 June 2021 in an environmentally sustainable manner. Over recent decades, there has been a 30% Published: 28 June 2021 decrease in European pasture land usage as a result of the increased levels in production ef- ﬁciency and feed controllability that are achievable with conﬁnement based systems, where Publisher’s Note: MDPI stays neutral animals are fed indoors [4,5]. Conversely, pasture-based systems in suitable climates have with regard to jurisdictional claims in the potential to be more economically and environmentally sustainable than conﬁnement published maps and institutional afﬁl- systems. However, pasture-based systems are hindered by reduced feed controllability iations. due to the spatial and temporal heterogeneity of grassland swards [6,7]. The quantity of herbage available for grazing within pastures can vary between 15% and 60% as a result of selective grazing, dung pats, and seasonal changes in sward morphology, making it difﬁcult to accurately measure and allocate for grazing [8–10]. Copyright: © 2021 by the authors. In Ireland, grazed grass is the predominant livestock feed source due to the suitability Licensee MDPI, Basel, Switzerland. of the temperate climate for grass production [11]. The Irish climate provides optimum This article is an open access article conditions for grazing, enabling cows to graze in excess of 300 days per year, which distributed under the terms and allows Ireland to produce milk and beef at a relatively low cost and in a sustainable conditions of the Creative Commons manner [12,13]. Maximising pasture utilisation through optimal grassland management is Attribution (CC BY) license (https:// vital in terms of ensuring the economic sustainability and mitigation of the environmental creativecommons.org/licenses/by/ impact of pasture-based livestock production. A pasture-based system that can maintain 4.0/). Agriculture 2021, 11, 600. https://doi.org/10.3390/agriculture11070600 https://www.mdpi.com/journal/agriculture Agriculture 2021, 11, 600 2 of 38 concentrate and nitrogen (N) fertilizer levels while increasing grass utilisation and stocking rate will increase N use efﬁciency [14,15]. Efﬁcient and sustainable pasture-based livestock production is primarily based upon synchronising the herd’s dietary requirements with seasonal grass production rates. This allows for the maximum level of fresh grass to be utilised through an increased daily intake of high-quality fresh grass dry matter (DM) per grazing animal [16,17]. Increasing grass utilisation has major ﬁnancial beneﬁts, as fresh grass is the cheapest feed source on Irish ruminant livestock farms [18]. Currently, the average Irish dairy farmer is utilising approximately 7–8 t DM ha of grass per annum, but has the potential to utilise 12–16 t DM ha [19–21]. The value of increasing 1 1 1 grass utilisation has been estimated to be up to 173 tonne ha year [17]. Frequent and accurate measurement of grass quantity and quality is one of the main methods of maximising grass utilisation and production on pasture-based farms [20,22,23]. Optimal grassland management is highly dependent on the accuracy of information on pasture quantity and quality that is available to the farmer [16,24]. Precise grass allocation to the herd is integral to optimal grassland management. Excess allocation of grass leads to wastage and quality degradation within a pasture. Alternatively, not providing sufﬁcient herbage to the herd results in decreased milk and beef production [25]. Grass is quantiﬁed and allocated in terms of herbage mass (HM), which is the unit weight of DM per hectare 1 1 (ha ) and is measured in units of kg DM ha . Several non-destructive methods and tools for measuring grass quantity have be- come popular on Irish farms in recent decades. There are a range of issues with these methods with regard to operator bias, precision, and difﬁculties in accounting for spatial variation [26–28]. Another signiﬁcant issue concerning current grass measurement prac- tices is the absence of a deﬁnitive protocol for grass measurement that farmers can use to objectively measure their grass and to account for the variation of grass growth within paddocks [29]. In terms of grass quality, there are no established on-farm methods which a farmer can use to estimate the quality of grass within their pasture. Pasture qualitative analysis methods are typically laboratory based and involve time consuming pre-processing proce- dures, such as grinding and oven drying, which can take several days to complete. Grass quality parameters that are considered important for grazing systems include DM, dry matter digestibility (DMD), metabolisable energy (ME), organic matter digestibility (OMD), crude protein (CP) and water-soluble carbohydrates (WSC) [11,30]. Signiﬁcant potential exists for improving the availability and accuracy of grassland measurement information by means of precision agriculture (PA) technologies. The concept of PA is focused on the acquisition of precise ﬁeld data at a spatial and temporal scale that would capture pasture variation and enable targeted responses, with the aim of increasing economic returns and reducing environmental impacts [31]. Precision technologies are a relatively new phe- nomenon with regard to grassland management compared with larger, more industrial scale agri-systems such as cropland industries [24]. Schellberg et al. [31] outlined reasons for the comparatively slow uptake of PA technologies with regard to grassland farming. The most signiﬁcant factors included the greater diversity within grassland, in terms of the spatial variation of soil and pasture characteristics, and the highly temporal dynamics of grass species. The aim of this review is to present an overview of the most seminal research pertain- ing to recent precision grassland measurement technological developments. The develop- ment of such technologies will be integral to achieving sustainable growth in grassland livestock industries in the future. This review is primarily focused on grass measurement systems that may be suitable for pasture-based livestock systems situated in temperate regions such as Ireland, although the research discussed is also applicable to global pasture- based industries. There has been no recent review of grass measurement technological developments relevant to Irish grassland. Relevant literature was initially collected using online databases prior to manual screening to select the most seminal research for inclusion in this review. Agriculture 2021, 11, 600 3 of 38 The ﬁrst section of this review outlines the methodology used to select the literature discussed in the subsequent sections. Secondly, signiﬁcant factors pertaining to grassland sward heterogeneity, conventional grass measurement methods, and the principles of pasture sampling will be discussed. Thirdly, this review will focus on state-of-the-art research on precision grassland measurement technologies. Finally, current challenges facing the development of precision grass measurement systems and the future of grassland measurement will be discussed. 2. Review Search Methodology and Literature Summary Searches for seminal literature were performed on Google Scholar [32] and ScienceDi- rect [33] using the following keywords: grass measurement, pasture, remote sensing, temperate grassland, perennial rye grass, clover, grassland heterogeneity, spectroscopy. Initially, studies from the period of 1970–2021 were selected (n = 313) to track developments in conventional grass measurement over the past 50 years and provide the context for the initial sections of this review. A subset of more recent studies from the period between 2001–2021 (n = 47) was then selected to identify seminal research relating to state-of-the-art developments in grass measurement technology, which are discussed in the latter sections of this review. The literature dataset was then manually reﬁned to exclude non relevant and duplicate studies. Inclusion criteria were: studies must contain original peer-reviewed research and be published as either scientiﬁc journal articles or conference papers; research was conducted on temperate pasture, preferably including perennial rye grass perennial ryegrass (Lolium perenne L.) (PRG) and/or white clover (Trifolium repens L.; clover) (WC) and articles were in the English language. Exclusion criteria included research conducted on arid or tropical grassland with no relevance to Irish pasture and studies that had insufﬁcient information regarding the error of assessed measurement systems. Additional studies were located by tracking references and citations from the most relevant literature in the initial dataset. The reﬁned literature dataset contained n = 99 studies relating to the measurement of temperate grassland. A summary of the dataset indicating the technologies used, region, grass species and scale of the selected studies can be viewed in Appendix A. The selected studies are discussed in the following sections of this review. 3. Grassland Sward Heterogeneity The availability of herbage for grazing can vary considerably within pastures, which makes it difﬁcult to accurately quantify and allocate on a regular basis. Sward heterogeneity in terms of both quantity and quality can increase as a result of a number of factors, including soil, environmental, temporal, compositional, and grazing conditions. Jordan et al. [34] recorded mean variation in HM to be in the range of 15–30% on intensively cut PRG dominant silage ﬁelds in the North of Ireland. This study further recorded increases in sward variation as the growing season progressed in accordance with the morphological growth stages of PRG. Heterogeneity is typically higher within grazed pastures compared with silage ﬁelds, due to selective grazing by animals, which increases the difﬁculty of estimating average HM [9]. Barthram et al. [8] recorded variation in sward height in the range of 30–70% due to selective grazing on PRG dominant swards grazed by sheep in Scotland. Klootwijk et al. [9] quantiﬁed that the area of rejected patches of pasture ranged from 22% to 43%, which increased as the grazing season progressed in Dutch PRG pastures, and recommended that the area of rejected patches be accounted for when calculating available HM. Murphy et al. [35] found that average pre-grazing HM variation was 36% over a grazing season within Irish PRG dominant dairy pastures. A summary of reported values of sward heterogeneity in terms of grass quantity can be seen in Table 1. A further cause of pasture variation and damage is poaching. In wet conditions, tread- ing pressure from animals remoulds the soil surface damaging the sward and compacting the soil, which can increase weed ingress and reduce pasture production. Grassland man- agement factors, such as stocking rate and herbage allowance, also have signiﬁcant impacts on the variation of sward yield and quality within a pasture [36]. Sward composition, in Agriculture 2021, 11, 600 4 of 38 terms of both species and morphology, is another signiﬁcant factor with regard to pasture heterogeneity. Mixed swards of WC and PRG are becoming more popular on Irish farms due to their noted environmental and grazing beneﬁts [37,38]. Clover has a lower canopy height than PRG, resulting in mixed swards having greater variation in canopy surface height, which can make measurement more difﬁcult [39]. Multi-species swards, including plants such as chicory (Cichorium intybus L.) and plantain (Plantago lanceolata L.), may become more frequently used on grassland pastures in the near future with the objectives of increasing quality and reducing N requirements [40]. Similar to PRG/WC swards, multi-species swards may have greater variation in canopy height and structure compared with PRG monocultures. Furthermore, the morphological growth stage of the PRG plant has a major effect on sward structure and variation. The main components of the PRG plant (leaf, stem, and dead leaf proportions) vary considerably depending on the morphology of the plant, time of year, and grazing management [41]. Temporal and morphological effects have further signiﬁcant impacts on sward quality variation. Wilkinson et al. [23] found that variation in most sward quality components increased rapidly as the sward entered its reproductive growth stage, with variation at a maximum in the middle of the grazing season in British pastures. The study also found that within month variation in sward quality was large, resulting in either inadequate or excessive amounts of essential nutrients being provided to grazing animals and recommended that regular sward quality measurements be taken to allow for more optimum feeding of animals. Table 1. Summary of reported values of mean sward heterogeneity in terms of pre-grazing grass quantity on temper- ate grasslands. Grass Measurement Sward Study Year Region Sward Type Species Parameter Heterogeneity * Murphy et al. [29] 2020 PRG/WC Ireland Dairy pasture HM (kg DM ha ) 36% Jordan et al. [34] 2003 PRG Ireland Silage ﬁeld 25% HM (kg DM ha ) Murphy et al. [29] 2020 PRG/WC Ireland Dairy pasture CSH (mm) 29% Klootwijk et al. [9] 2019 PRG The Netherlands Dairy pasture CSH (mm) 28% Barthram et al. [8] 2005 PRG/WC Scotland Sheep pasture Height (mm) 46% * Sward heterogeneity = coefﬁcient of variation of measurement parameter, HM = herbage mass, CSH = compressed sward height, Height = standing sward height, PRG = perennial rye grass, WC = white clover. 4. Conventional Grass Measurement Destructive measurement refers to when herbage is cut and removed from the pasture for direct analysis. Destructive techniques are typically used as reference methods for modelling herbage parameters by means of non-destructive measurement methods. The ‘gold standard’ method of determining HM is by cutting and weighing herbage samples using a quadrat, shears, and scales [16,42]. Despite cutting and weighing being the refer- ence method for determining HM there are numerous potential sources of measurement error including operator bias regarding sample area selection and post cutting height. Furthermore, there are several well documented disadvantages to cutting and weighing including labour intensity and herbage destruction [42–44]. A signiﬁcant disadvantage of cutting and weighing is the requirement of a large number of samples to account for sward spatial variation within grazed pastures. Sward heterogeneity can be accounted for by increasing sampling intensity. However, this leads to increases in measurement labour and time, as well as increases in the quantity of herbage removed from the pasture [45–47]. Non-destructive measurement refers to when grass is analysed in-situ and modelling techniques are used to predict selected parameters. This form of measurement enables real- time analysis of pasture and ultimately allows for more responsive grassland management decision making. Non-destructive measurement techniques are typically cheaper, less laborious, and more practical than destructive methods. For these reasons non-destructive Agriculture 2021, 11, 600 5 of 38 techniques are more commonly used by farmers on a regular basis. However, modelling techniques are prone to error and non-destructive methods are typically less accurate than destructive methods. Visual estimation is the most fundamental method of non-destructive grass measure- ment. It involves the farmer observing the pasture and estimating the average HM within a paddock. It is the fastest, cheapest, and least laborious method of measuring HM. The farmer is able to use their knowledge of the sward’s composition to account for the varia- tion in HM within the pasture [48,49]. The most signiﬁcant issue with visual estimation is that it is highly subjective and variations in herbage estimations between observers have been noted to be large [50–52]. The most established non-destructive tool for measuring pasture in Ireland is the rising plate meter (RPM) [20,29]. The RPM records a combined measure of pasture height and density, referred to as compressed sward height (CSH), using a weighted disc attached to a scaled staff that is dropped onto the sward. Recorded CSH is then used to model HM. Use of the RPM requires minimal training and a large number of samples can be recorded and distributed throughout a paddock in a relatively short time duration [53,54]. A recent iteration of the RPM has been developed in Ireland [55] that uses a GPS integrated ultrasonic sensor to record the height of the rising plate (Figure 1). The main advantages of this RPM over conventional models are its rapid data processing capabilities via automated links to online decision support tools (DST) and its ability to geo-tag measurement data. Figure 1. Schematic of ultrasonic rising plate meter developed by McSweeney et al. [55]. Despite the RPM being an established grass measurement tool, its limitations in terms of accuracy have been noted. A considerable source of RPM error is the large variation be- tween CSH measurements recorded within pastures, resulting from the interaction between the rising plate and the heterogeneity of the vertical proﬁle of the sward. Factors reported to affect this interaction include grass species, season, and grazing intensity [28,47,56]. There is no standardised RPM design and models vary considerably in terms of plate pressure and measurement system. This makes it difﬁcult to transfer established HM cali- brations between different RPM models [57]. Despite the RPM being designed to reduce the subjectivity of grass measurement, there is no robust measurement protocol on how to use the RPM in an objective manner and this can contribute to measurement variation. A recent study by Togeiro de Alckmin et al. [58] on controlled PRG trial plots in Tasmania found that the RPM had a root mean square error (RMSE) of 522 kg DM ha . A similar Agriculture 2021, 11, 600 6 of 38 study in Ireland on PRG dominant trial plots and grazed paddocks reported RPM errors 1 1 of 354 kg DM ha and stated that this error could be reduced to 243 kg DM ha by combining RPM measurement with grassland management and meteorological data by means of machine learning [59]. The study further included a comprehensive review of RPM HM prediction error and its sources. A further study estimated the combined effects of both measurement and calibration error for the RPM to be 28.1% relative prediction error (RPE), when a robust measurement protocol was adhered to [35]. 5. Pasture Sampling Techniques To account for pasture heterogeneity multiple samples or measurements may need to be taken at locations distributed throughout a paddock following a predetermined protocol [34,45,60]. The effectiveness of a sampling protocol can be deﬁned by its accuracy, precision, and level of potential bias [43]. To determine an absolute mean parameter value for a pasture, the entirety of the herbage within that pasture would need to be harvested and analysed. This may be possible on small, controlled trial plots used in research but is not practical on grazed paddocks. Therefore, the best possible representation of the absolute mean must be determined, henceforth referred to as the ‘true’ mean. Accurately estimating the ‘true’ mean of any herbage parameter can be difﬁcult owing to the heterogeneous nature of grazed swards. A signiﬁcant source of measurement error is inconsistent operator use, which is deﬁned in terms of reproducibility or operator bias [61]. Bias error can be minimised by adhering to a robustly designed sampling protocol. Once a pasture measurement tool is used in accordance with manufacturer guidelines, bias in terms of sample area selection remains the greatest source of unknown bias. For example, when measuring a pasture area, an operator may select the shortest path between the pasture entry and exit points and take all of their samples along this path, as this is most convenient. This path may not give an accurate representation of the variation of herbage within the pasture and is therefore biased by the operator ’s desire for convenience. Likewise, the operator may consciously or subconsciously select sample locations with either consistently high or low proportions of herbage. Similarly, an operator might choose to sample a paddock along transect lines (Figure 2a) in an attempt to distribute samples more evenly. This method is also biased by the operators preference with regard to the positioning of each transect line. There is no deﬁnitive protocol for objective pasture sampling or measurement on Irish pastures. With regard to the RPM, measurements are typically carried out 25–50 times in transects or in a ‘W’ pattern (Figure 2b) throughout a paddock [27,43,52]. To avoid operator bias and maximise measurement precision, sample locations should be randomly selected and spatially balanced throughout a pasture, although this can be difﬁcult to implement in practice. If sample location selections are totally random, the entire area within a paddock has an equal probability of selection. Measurement parameter values can be treated as random variables and statistical analysis can be employed to determine parameter mean and estimation error without bias [44,62]. Increasing sampling area and resolution may increase measurement precision, how- ever, this further increases sampling time and cost. There is a trade-off between the beneﬁt of increasing accuracy versus time and cost. Reducing measurement time and effort is vital, not only in saving labour costs for farmers, but also to encourage more farmers to measure grass on a regular basis. The time and cost requirements of regular and accurate grass measurement are signiﬁcant barriers to promoting grass measurement on farms. A study conducted by Creighton et al. [21] showed that only 20% of Irish dairy farmers used technology to measure grass on a regular basis. Deming et al. [63], in a study of Irish dairy farms that were classiﬁed as labour efﬁcient, found that farmers spent between 0.28 1 1 and 0.41 h cow year at grass measurement. Behavioural studies by Hall et al. [64] in Tasmania and Eastwood et al. [65] in New Zealand, reported that farmers reported a lack of conﬁdence in accuracy and regarded measurement time and effort as major barriers to the adoption of measurement tools for pasture management. Agriculture 2021, 11, 600 7 of 38 Figure 2. Illustration of (a) ‘X’ transect; (b) ‘lazy W’; (c) simple random; and (d) random stratiﬁed sampling pasture measurement protocols on 1 ha grazed pasture, with orange circles indicating measurement locations (n = 20) and blue dashed line outlining the measurement route for (a) and (b). The requirement for the development of a universal pasture sampling methodology to reduce operator bias, give more precise representations of spatial variation, and min- imise measurement labour has long been acknowledged [34,46,56]. O’ Sullivan et al. [54] presented a combined technique of quadrat cuts and RPM measurements with the aim of reducing the number of herbage cuts required (by 50%) to accurately predict ‘true’ HM for research purposes on Irish PRG pastures. Thomson et al. [52] outlined the need for HM measurement protocol standardisation between dairy research centres in New Zealand and recommended that 50–80 RPM measurements be taken per paddock. Nakagami [10] developed a method to assess HM in Japanese pastures by RPM sampling just two areas per paddock, but when validated on commercial paddocks, only half of the estimates were found to be within 20% of ‘true’ mean. Hutchinson et al. [66] prototyped a pasture sampling protocol for the RPM in the form of a decision tree that could be easily understood by farmers, outlining the required number of RPM measurements in relation to an operators desired level of precision. The study found that a depreciating exponential relationship existed between RPM measurement rate and HM prediction error and recommended random stratiﬁed sampling (RSS) as an accurate method of pasture sampling. Similar rela- tionships between grass sampling rate and error have been reported by Jordan et al. [34], O’ Sullivan et al. [54], and Murphy et al. [29] on Irish PRG swards. Using quadrat cuts, Jordan et al., [34] recommended a sampling rate of 7 cuts ha based on the principle of RSS, to estimate ‘true’ mean HM to within 5% error and enable yield mapping of spatial heterogeneity within silage ﬁelds. A study by Murphy et al. [35] utilised RSS to developed a grass measurement optimisation tool to generate accurate and efﬁcient grass measurement protocols and concluded that eight measurements ha was an optimum sampling rate for the RPM. Agriculture 2021, 11, 600 8 of 38 The RSS method involves dividing the target measurement domain into several equally sized strata and then assigning an equal number of samples randomly within each stratum, as seen in Figure 2d. This allows for a more efﬁcient distribution of samples within the domain in comparison with simple random sampling (Figure 2c) and average spatial variation within and across strata can be estimated without bias [43,63,67]. The implemen- tation of robust sampling protocols in conjunction with GPS technology enables the use of geostatistical procedures such as Kriging interpolation, which can be used to develop parameter heat maps of a pasture for spatial analysis and PA applications [63,67]. Accurate geo-referenced measurement information of sward quantity and quality would enable the use of variable rate fertilisation systems to reduce cost, GHG emissions, and nutrient leaching to waterways. Moreover, such data could lead to more precise spatial analysis of sward characteristics and ultimately lead to increases in pasture utilisation [24,68,69]. 6. Grass Quality Analysis by Means of Near Infrared Spectroscopy Most conventional grass quality measurement methods require herbage samples to be taken from the ﬁeld and analysed in the laboratory. One of the more established and rapid methods of herbage quality analyses is near infrared spectroscopy (NIRS). Conventional lab-based NIRS required removal of herbage samples from the ﬁeld and pre-processing of the samples prior to analyses. More recent NIRS developments have focused on reducing the need for sample removal and pre-processing. Sample removal can be avoided by means of in-situ or portable NIRS analysis. The main advantages of NIRS are that it is a more rapid analysis technique and it has no chemical input requirements compared with traditional wet chemistry analysis procedures. Disadvantages include the initial cost of purchasing an NIRS spectrometer and its reliance on chemometric modelling techniques, which are prone to error. Near infrared (NIR) light energy has characteristic wavelengths ranging between approximately 700 and 2500 nm on the electromagnetic spectrum [70,71]. Near infrared spectroscopy analysis measures the absorption rates of low energy infrared light radiation within matter, which are then used to quantify the chemical constituents of said matter by means of empirical modelling methods, referred to as chemometrics. Analyses of dried and milled forage quality by means of NIRS is well established within the agri-food industry [72–74]. More recently, NIRS quality prediction calibrations have been derived for dried and milled grass for research purposes in Ireland, such as identifying desired traits for different grass varieties [30,75,76]. Recent research has focused on applying NIRS to predict quality parameters of fresh herbage with the aim of further reducing laboratory workloads by eradicating the need for sample pre-processing, which can also have detrimental effects on sample composition [77]. Spectroscopic analysis of fresh forages and grasses is largely restricted by the high presence of moisture, which results in large spectral peaks that overshadow spectral identiﬁers for numerous quality traits, such as CP [77–79]. Despite this, breakthroughs have been made with regard to NIRS analyses of fresh forage and grass using conventional NIR instruments. Thomson et al. [80] investigated if a pre-existing fresh grass silage NIRS calibration could predict quality in grass/clover silage samples in the UK. The study found that some parameters such as DMD could be predicted with acceptable accuracy. However, bias for parame- ters such as CP increased with clover content and clover speciﬁc calibrations performed better. Alomar et al. [81] concluded that reﬂectance NIRS could accurately predict the 2 1 compositional components, including DM (R = 0.99, SE = 6.5 g kg ) (SE = standard 2 1 error) and CP (R = 0.91, SE = 18.4 g kg ), of a variety of fresh grass swards in Southern Chile. Dale et al. [82] developed fresh grass NIRS calibrations to investigate optimum sampling and storage techniques on Irish PRG dominant pastures and reported R val- 1 1 ues of 0.92 (SE = 0.95 g kg fresh weight ), 0.90 (SE = 0.543 g kg fresh weight ) and 0.79 (SE = 0.622 g kg fresh weight ) for DM, N and WSC, respectively. Lobos et al. [83] reported good prediction performance (R 0.84) for fresh grass NIRS analysis for param- eters DM (RMSE = 1.13%) and CP (RMSE = 2.22%), in comparison with low prediction performance (R 0.78) for DMD (RMSE = 2.41%), OMD (RMSE = 2.61%), and WSC Agriculture 2021, 11, 600 9 of 38 (RMSE = 0.06%) in Chilean permanent pasture. A summary of the accuracy of relevant NIRS calibrations for grass quality is presented in Table 2. A more recent study by Murphy et al. [84] presented NIRS calibrations that could predict DM with a high degree of accuracy 2 1 2 1 (R = 0.86, SE = 9.46 g kg ) and CP with moderate accuracy (R = 0.84, SE = 20.38 g kg ) in Irish PRG swards. The development of rapid NIRS calibrations to predict fresh grass quality would signiﬁcantly reduce laboratory labour, inputs, and cost. Furthermore, fresh grass NIRS would enable more precise grassland and feed management decisions to be made on a daily basis. Table 2. Summary of NIRS grass quality studies and calibration statistics relevant to temperate grassland presented in Murphy et al. [84]. Sample Error (g Study Analyte Region Species Parameters R RPD No. kg ) Murphy et al. Fresh grass Ireland PRG DM, CP 1812 0.85, 0.84 9.5, 20.4 2.57, 2.37 (2021) Lobos et al. Permanent Fresh grass Chile DM, CP 915 0.93, 0.84 11.3, 22.2 3.7, 2.5 (2019) pasture Parrini et al. Natural Fresh grass Italy DM, CP 100 0.87, 0.88 2.75, 2.14 2.75, 2.26 (2019) pasture Bonnal et al. Mixed Fresh grass France CP 103 0.93 1.55 1.97 (2013) swards Alomar et al. Mixed Fresh grass Chile DM, CP 107 0.99, 0.91 6.55, 18.4 7.15, 3.69 (2009) swards McClure et al. Fresh grass USA Fescue N 31 0.88 6 - (2002) Park et al. Fresh grass silage Ireland - DM, N 136 0.85, 0.78 23.3, 28.1 -, 4.8 (1998) Burns et al. Dried & milled Ireland PRG CP 2076 0.98 5.1 - (2014) grass Jafari et al. Dried & milled Ireland PRG CP 153 0.96 6.8 - (2003) grass 1 2 PRG = perennial rye grass, DM = dry matter, CP = crude protein (g kg DM), N = nitrogen, R = coefﬁcient of determination, Error = standard error of cross-validation, standard error of prediction or root mean squared error depending on study, RPD = ratio of percent deviation, ‘-‘ = denotes where data was not published as part of study. In the past two decades, NIRS technological developments in the area of diode array spectrometers and micro-electric-mechanical-systems (MEMS) have allowed new possibili- ties regarding real-time in-situ NIRS analysis of fresh grass [85,86]. Portable spectrometers have numerous advantages over lab-based systems including, in-situ measurement, lower costs, real-time results and non-destructive sampling. Portable NIRS has noted limitations regarding light noise, particle size, wavelength range and moisture effects [86]. A high speed and durable portable spectrometer has been developed for the selection of grass species for breeding purposes [87]. This NIRS sensor was capable of predicting DM of fresh grass, with an acceptable correlation in relation to wet chemistry analysis (R = 0.73), in real-time and was built into a grass plot harvester. Mendarte et al. [88] outlined the potential for using portable NIRS to determine the quality of standing mountain pasture in the Basque Country, reporting reasonable prediction results for DM (R = 0.82, SECV 1 2 = 0.56 g kg ) (SECV = standard error of cross validation) and CP (R = 0.62, SECV = 1.50 g kg DM) in relation to laboratory reference analysis. Reddersen et al. [79] assessed the use of portable NIRS to evaluate the feed quality of mixed species standing swards in Germany and concluded that it was only capable of predicting approximate values (R = 0.72, SECV = 3.9 g kg DM) of N content, due to the high presence of moisture and low levels of sample homogeneity. Smith et al. [89] used a similar technology in an Australian Agriculture 2021, 11, 600 10 of 38 PRG breeding programme and recommended that portable NIRS was feasible as a high speed and low cost method of evaluating nutritive value for parameters CP, DM, DMD, WSC, acid detergent ﬁbre, and neutral detergent ﬁbre, reporting R values ranging between 0.49 and 0.89 and RMSE values between 1.84% and 3.41%. An issue that constrains the development of portable NIRS applications is that many portable spectrometers on the market are ‘closed box’ systems and researchers do not have access to the calibration data within them [86]. In recent years, an on-line NIRS device for silage and pasture quality assessment has been developed in the UK (NIR4) (Figure 3). The NIR4 is capable of scanning fresh pre-cut grass and uploading the spectral data to the user ’s handheld smart device for rapid analysis, with calibrations for parameters DM, CP, WSC and DMD [90]. However, no published data on the precision of this system could be found in the literature. A study by Patton et al. [91] assessed the efﬁcacy of three portable NIRS sensors from different manufacturers to analyse quality traits of PRG swards in the North of Ireland. They concluded that any of the instruments tested could not replicate quality predictions made from a lab based NIRS spectrometer. Hart et al. [92] reported high levels of systemic error (9–22%) using portable NIRS on Swiss mixed swards. There is considerable scope for portable NIRS applications in grassland farming. More research needs to be performed on environmental, moisture, and sample particle heterogeneity effects to establish the feasibility of portable NIRS. Figure 3. Image of NIR4 grass quality analysis system reprinted from ref. [90]. 7. Terrestrial Sensing In the context of this review, terrestrial sensing refers to non-spectral sensors that interact with the sward at (or close to) ground level. Terrestrial on-the-go soil electrode sensing has been used by Vogel et al. [93] to investi- gate potential relationships between soil PH, moisture content, and the spatial variation of herbage mass on grazed German pasture. The study utilised a tractor mounted Veris mobile sensor platform (Figure 4) for rapid soil analysis and apparent soil electrical conductivity was measured to predict soil moisture content. Agriculture 2021, 11, 600 11 of 38 Figure 4. Tractor mounted Veris mobile sensor platform reprinted from ref. [93] for on-the-go soil analysis on grazed pasture. The use of a sensor to directly measure sward height using ultrasonic waves has been investigated on mixed species German swards by Reddersen et al. [94], who found that it 2 1 predicted HM with reasonable accuracy (R = 0.73–0.76, RMSECV = 0.88–1.17 t DM ha ) (RMSECV = root mean squared error of cross-validation). The study further found that combining ultrasonic sward height (USH) and remote sensing data in a multi-sensor (leaf area index and hyperspectral sensors) approach increased HM prediction accuracy by 30%. An earlier study by Fricke et al. [95] investigated combining USH with GPS on a vehicle for real-time ‘on the go’ measurement and rapid yield mapping of pasture, as seen in Figure 5a. A number of USH measurement limitations were highlighted in that study, including poor precision caused by the wide ultrasonic response area and poor responses to changes in sward geometry and heterogeneity. The study further outlined the potential for combining arrays of low cost USH sensors, which could be ﬁtted onto tractors or mowers to generate cheap and minimal effort HM predictions. Safari et al. [96] compared the use of mobile USH and spectral sensing (Figure 5b) with static sensing, reporting lower prediction accuracy for mobile measurement due to positional errors caused by variation in the ground proﬁle. Moeckel et al. [97] found poor results (R = 0.36–0.74, SE = 675–1118 kg DM ha ) for predicting HM using USH on mixed species swards, reporting high errors in mature swards as a result of patches of rejected grass left after grazing. The study further investigated the potential for combining spectral data from spectrometers and satellites with USH and found that utilising both visible and NIR spectral 2 1 data improved HM prediction performance (R = 0.66–0.88, SE = 485–866 kg DM ha ). A similar USH measurement system that could be ﬁxed to a farm vehicle to measure pasture height while traveling at speeds of 20 km h achieved HM prediction accuracies of 2 1 R = 0.75 and SE = 270–350 kg ha on New Zealand grassland [98]. Apparent advantages of USH sensing for grass measurement are that it is relatively fast, low cost, and simple to implement, with the potential for mobile application. Conversely, limitations exist with regard to the precision of USH as a result of high variation in signal responses to canopy heterogeneity. The C-DAX Pasturemeter is a terrestrial sensing device for predicting HM that has been developed and is in common use in New Zealand. The C-DAX is mounted on wheels and is designed to be towed behind a quad bike at approximate speeds of 20 km/h, as illustrated in Figure 6. This device measures pasture height using light emitting and sensing photodiode arrays. As the C-DAX is towed through the pasture the photodiode sensors record a height proﬁle of the pasture. Studies have concluded that measuring pasture standing height has notable limitations with regard to predicting HM in comparison with the RPM [26,27]. Despite this, the C-DAX has one signiﬁcant advantage over the RPM. The C-DAX is capable of acquiring much more data (200 measurements per second) in a more rapid manner than the RPM without the need of walking [99,100]. King et al. [101] compared the measurement accuracies of the C-DAX and RPM over a range of pastures in New Zealand throughout a single grazing season. Results in terms of RMSE ranged 1 1 between 576 and 655 kg DM ha for the C-DAX and 441 and 552 kg DM ha for the Agriculture 2021, 11, 600 12 of 38 RPM. Oudshoorn et al. [102] discovered that the C-DAX predicted HM to within acceptable accuracy (R = 0.76) on Danish PRG/WC swards. The prediction error calculated by Schori [103] was slightly higher for the C-DAX (SE = 311 kg DM ha ) compared with the RPM (SE = 285 kg DM ha ), on Swiss mixed swards over three grazing seasons. The C-DAX also has in-built GPS geo-tagging capabilities, which have been utilised to generate yield maps for targeted pasture management applications [104]. Currently, the C-DAX is not commonly used by Irish grassland farmers. This may be due to a perception that predicting HM by measuring standing sward height is not as accurate as CSH because it is not as sensitive to sward density, as outlined by Shalloo et al. [24]. Figure 5. (a) Schematic of ‘on the go’ grass measurement system presented in Fricke et al., reprinted with permission from ref. [95]. Copyright 2021 Elsevierand (b) image of similar system reprinted from ref. [96]. Figure 6. Elevation and cross section schematic of the C-DAX Pasturemeter. Terrestrial sensing of pasture may enable grass measurement to be conducted by autonomous ground vehicles (AGV), which work within close proximity to the ground in a remote manner. Research into these vehicles for PA applications has predominantly been focused on the arable sector. A more recent novel modiﬁcation of the C-DAX is a proposed pasture robot currently under development in New Zealand [105]. The concept combines an AGV with the C-DAX system. The robot is designed to autonomously navigate from a central charging station to a paddock and traverse the pasture using a pre-programmed sampling strategy, negating the need for physical labour. The entire area of a 2-ha paddock could be sampled for ﬁeld mapping purposes within 5 h, or a representative area of the same paddock could be sampled for basic grassland management purposes in under 30 min. Potential for ﬁtting soil sampling and grass quality sensors to this system is also being considered. Gobor et al. [106] proposed a similar pasture robot system for use on Agriculture 2021, 11, 600 13 of 38 German pastures. Their concept incorporates a mulcher system on the robotic platform (Figure 7) so that areas of rejected pasture, identiﬁed by a sward height sensor on the robot, can be mulched to encourage the regrowth of high-quality pasture. Likewise, areas of poor HM could be treated with a seeder incorporated on the proposed robotic rover platform. Sampling protocol design would need to be a signiﬁcant consideration with regard to the potential use of AGVs for pasture measurement. The design of optimum AGV sampling protocols for pasture measurement would need to be in line with best practice for pasture sampling. A signiﬁcant advantage of an AGV system would be that measurement labour and time do not place the same level of constraint on protocol design. Conversely, when compared with unmanned aerial vehicle (UAV)-based remote sensing, AGV systems have a number of disadvantages, including slower data collection, damage to sward caused by movement paths, and higher cost. Theses disadvantages may be offset by the higher resolution of measurement data and reduced climate noise interference that is achievable using AGVs when compared with remote sensing [107,108]. Figure 7. Pasture robot system concept proposed by Gobor et al. [108] incorporating mulcher and seeder. 8. Proximal Spectral Sensing In the context of this review, proximal spectral sensors refer to spectral sensors that operate within 2 m of the soil surface, as deﬁned by Viscarra Rossel et al. [109]. Proximal spectral sensing includes the previously discussed portable NIRS technologies, but the following section deals with all other prevalent proximal spectral sensing technologies. Hyperspectral sensing (HS) has the ability to capture a wide range of spectral data, ranging from the visible to NIR light regions, which results in greater availability of data for prediction modelling in comparison with NIRS. Devices for HS can be handheld for manual proximal sensing or mounted on un-manned aerial vehicles and satellites. Disadvantages of HS include the capture of a large amount of data that is redundant for modelling and the high cost of instrumentation [94]. Similar to NIRS, HS data can be used to model pasture quantity and quality using chemometric modelling techniques. Pullanagari et al. [110] used a HS canopy probe sensor (500–2400 nm) to predict a range of in-situ standing sward quality characteristics on PRG/WC dominant swards in New Zealand. The study achieved 2 2 satisfactory prediction results for CP (R = 0.78, RMSE = 2.33% DM), ME (R = 0.83, RMSE = 1 2 0.46 MJ kg ), and OMD (R = 0.83, RMSE = 4.02% DM). The samples used were not spread across an entire growing season and reference analysis was conducted by lab based NIRS. Hyperspectral sensing enables the prediction of sward characteristics by more basic means of spectral modelling referred to as vegetation indices (VI), which are commonly used for remote sensing applications. One of the most used VI is the normalised deference vegetation index (NDVI), which estimates the quantity of vegetation present by the ratio of red and NIR light wavelengths that are absorbed by pasture photosynthesis [24]. Another commonly researched VI is the leaf area index (LAI), which is a measure of the sward foliage area against ground area [94]. Reddersen et al. [94] found poor results for HS prediction Agriculture 2021, 11, 600 14 of 38 2 1 of HM using LAI (R = 0.36–0.44, SE = 1.5–1.8 t DM ha ) using the HS conﬁguration illustrated in Figure 8. The study further investigated the use of HS imagery (350–2500 nm) to predict HM by means of chemometric modelling with more positive results (R = 0.70– 0.89, SE = 0.66–0.85 t DM ha ). Moeckel et al. [97] discovered that normalized difference spectral index (NDSI) in combination with USH signiﬁcantly improved HM prediction 2 1 (R = 0.52, SE = 1000 kg DM ha ). Results for HS (305–1700 nm) prediction of HM were 2 1 poor (R = 0.48, SE = 950 kg DM ha ) and limitations in HS caused by the high presence of senescent material in the sward were observed later in the growing season. Ancin- Murguzur et al. [111] found a signiﬁcant correlation between HS and HM on Norwegian 2 2 mixed species swards (R > 0.55, RMSE 180 g m ), but noted increased error due to environmental inﬂuences on spectral signatures observed in cloudy and wet conditions. The study further showed that spectral data captured in the range of 350–900 nm was more robust against the inﬂuences of moisture. Pullanagari et al. [112] found strong correlations for CP (R = 0.65–0.83) on dairy pasture in New Zealand using HS. Askari et al. [113] found 2 1 2 positive results for predicting HM (R = 0.88, RMSE = 160 kg DM ha ) and CP (R = 0.82, RMSE = 10.0 g kg DM ) using a handheld HS camera on Irish PRG swards over two growing seasons. Figure 8. Schematic of hyperspectral sensing measurement system reprinted with permission from ref. [94].Copyright 2021 Elsevier. There are evident advantages to HS including non-destructive sampling, large sample area coverage, spatial variation identiﬁcation and potential incorporation with autonomous vehicles or tractor mounts. One of the main barriers to this technology is the high cost of HS devices, although this may decrease in the near future. Furthermore, HS and all other proximal spectral sensing technologies also have sampling issues with regard to accounting for spatial heterogeneity within swards. Agriculture 2021, 11, 600 15 of 38 9. Remote Sensing Remote sensing refers to all sensing techniques that operate at a distance greater than two meters from ground level [109]. This includes sensing methodologies that use UAVs, manned aircraft, and satellites. In the past decade, research on remote sensing methods for predicting grass yield and quality has increased. Remote sensing has the potential to cover larger sampling areas with minimal labour requirements. A range of remote sensing technologies can be ﬁxed to UAVs, which can ﬂy at low altitudes to obtain spectral data at high resolutions. Rueda-Ayala et al. [108] found weak correlations (R < 0.6) between red, green, blue (RGB) wavelength sensing data and HM on PRG dominant Norwegian swards and reported difﬁculties in measurement precision due to environmental factors such as wind speed, sunlight and cloud cover. Conversely, that study found that UAV sensing was less variable than terrestrial sensing data. Askari et al. [113] determined that red and green wavelength bands were important for predicting CP by means of UAV sensing on Irish PRG swards. Capolupo et al. [114] showed that UAV HS could predict sward height (R = 2 1 0.70–0.86, RMSE = 2.13–2.29 cm), HM (R = 0.36–0.83, RMSE = 2.95–3.81 kg DM plot ), 2 1 and CP (R = 0.56–0.76, RMSE = 11.73–12.28 g kg DM) on German controlled trial plots. Multi-spectral (MS) sensors that emit light radiation in discrete spectral bands and at broader resolutions than HS have been more commonly deployed in UAV research for pasture analysis. One major advantage of MS devices is that they are typically cheaper than HS instruments. Pullanagari et al. [115] reported reasonable precision (R = 0.6, 0.66, 0.68; RMSE = 2.88%, 065%, 5.27%) for parameters CP, ME, and OMD on New Zealand PRG dominant pastures over two grazing seasons using a proximal MS sensor, spanning 16 discrete wavelengths (460–1680 nm). A prominent issue with MS sensing was further highlighted in the study. Many MS sensors depend on natural light to illuminate the sward. Consequently, low atmospheric light intensity can cause sampling problems. Askari 2 1 et al. [113] reported good prediction results for HM (R = 0.78, RMSE = 215 kg DM ha ) 2 1 and CP (R = 0.77, RMSE = 13.6 g kg DM ) using UAV MS (Figure 9) on Irish PRG pastures over two grazing seasons. Togeiro de Alckmin et al. [58] reported that MS (R = 0.79, RMSE 1 1 = 405.8 kg DM ha ) had a 116 kg DM ha lower RMSE compared with the RPM for HM prediction, when an optimal selection of VI was used. Oliveira et al. [116] showed that a combination of HS sensing and 3D imagery out-performed MS measurements on Finish swards, accurately predicting silage sward HM (RPE = 14.6%), digestibility (RPE = 1.9%), and N content (RPE = 13.6%). A number of similar limitations have been reported for both proximal and aerial spec- tral sensing of pasture. The most signiﬁcant limitation is the heterogeneity of grassland, which is much greater than tillage, where remote spectral sensing has become more estab- lished. The temporal change in the ratio of photosynthetically to non-photosynthetically active (vegetative vs. dead) material in grassland swards has signiﬁcant effects on spec- tral absorption. Achieving adequate levels of spatial resolution to distinguish signiﬁcant variations in pasture performance for targeted management purposes is also an issue with pasture sensing. Sensors with sufﬁcient spatial and sensing resolution to identify pasture variation can be very expensive. Similar to NIRS, high moisture content within standing swards can obscure spectral features of certain quality parameters [112]. Light detection and ranging (LiDAR) is another potential technology that could be used in conjunction with UAVs for remote sensing of pasture. This technology utilises light beams (visible/infrared) emitted at a high irradiance rate to measure the distance and shape of terrestrial objects. The time it takes for each emitted light beam to be reﬂected back to the LiDAR sensor receiver is used to develop a point cloud dataset for each target object. Obanawa et al. [117] reported an average absolute error of 12 mm (10 mm) (R = 0.93) at a 20 mm resolution for LiDAR prediction of grass height on Italian ryegrass pasture in Japan. Disadvantages of LiDAR include its relatively high cost and susceptibility to high measurement error in windy conditions [118,119]. Moreover, the use of grass height to predict HM has further limitations as previously discussed. Agriculture 2021, 11, 600 16 of 38 Figure 9. (a) UAV with multispectral sensor and (b) UAV plot sensing ﬂy over from study by Askari et al. [113]. Several studies have investigated the potential of utilising satellite-based MS and HS to predict pasture quantity and quality [111,113,120]. The distinct advantages of satellite sens- ing relate to the larger spatial coverage, in terms of data acquisition, that can be achieved. The European Space Agency’s Sentinel-2 project comprises of two orbital satellites loaded with MS technology capable of monitoring land use variations at 10 m, 20 m, and 60 m resolutions [121]. Sibanda et al. [120] outlined how Sentinel-2 MS data could be used to predict HM with comparable accuracy to proximal HS on South African experimental 2 2 1 grassland plots (27–250 m )(R = 0.58, RMSE = 67.9 kg ha ). Askari et al. [113] reported 2 1 moderate success for predicting HM (R = 0.82, RMSE = 600 kg DM ha ) and poor results 2 1 for CP (R = 0.62, RMSE = 13.3 g kg DM) using Sentinel-2 data on Irish grassland plots (7.5 m ) and grazed paddocks ( 1 ha). The study illustrated that the overriding limitation for satellite spectral sensing on Irish pasture is frequent cloud cover, as data acquisition was not possible on days with over 30% cloud cover. An alternative technology for satellite remote sensing of pasture that may overcome cloud cover and illumination limitations is synthetic aperture radar (SAR), which uses high resolution radio wave reﬂectance to predict pasture height. Barrett et al. [122] utilized SAR to overcome cloud cover limitations for satellite classiﬁcation of Irish grasslands. A more recent study that used SAR on Irish PRG dominant dairy pasture (1 ha) yielded 2 2 promising results for both sward height (R = 0.55) and HM (R = 0.75) [123] at a 25 cm spatial resolution. However, research into this technology is still at an early stage. In light of the research outlined for terrestrial, proximal, and aerial sensing techniques, it is evident that longer, more detailed studies over numerous seasons and sward types need to be conducted before these technologies can become established within pasture-based agriculture. Results from the most recent research ﬁndings discussed in this review, which were most relevant to the measurement of temperate grasslands used for pasture-based livestock production (PRG/WC Irish pasture), are summarised in Table 3. Agriculture 2021, 11, 600 17 of 38 Table 3. Summary of grass measurement systems from the research discussed in this review that were most relevant to temperate (Irish) grasslands. Relevant Sample System Region Measure Prediction Herbage Quantity Herbage Quality Advantage Disadvantage Studies No. Conventional systems Error (g kg , g Error (kg DM 2 2 1 d e R R kg DM , % , 1 a b ha , mm ) % DM ) Murphy et al. Compressed Rapid, usability, Labour intensive, a , Rising plate meter Ireland HM 1977 0.77 354 * - - [59] sward height cost accuracy O’ Donovan et al. Perceived a, I Visual assessment Ireland HM 2205 0.95 193 - - Minimal labour High subjectivity [26] herbage cover Murphy et al. Spectral High cost, lab c d, I NIRS Ireland DM, CP 1812 - - 0.86, 0.84 9.46 , 20.38 Accuracy [84] absorption based, destructive State of the art Light sensing Sward surface a, I Schori [103] Switzerland HM 439 0.77 311 - - Rapid, automation Accuracy (C-DAX) height Obanawa et al. Sward surface Sward surface High cost, wind b, LiDAR Japan 25 0.93 - - Remote sensing 12 ** [117] height height error, accuracy Reddersen et al. Sward surface a, Ultrasonic Germany HM 167 0.76 880 * - - Rapid, automation Accuracy [94] height Victoria, Spectral DM, DMD, WSC 0.69, 3.14 , 2.70, 2.77, In-situ quality Portable NIRS Smith et al. [89] Aus- 540 - - Accuracy f, absorption CP 0.82,0.49,0.74 2.02 * analysis tralia Hyperspectral Askari et al. Spectral Remote sensing, a, d, Ireland HM, CP 84 0.88 160 * 0.82 10 * High cost sensing [113] absorption accuracy Multispectral Askari et al. Spectral Remote sensing, Lack of long term a, d, Ireland HM, CP 126 0.78 215 * 0.77 13.6 * sensing [113] absorption cost studies Satellite Askari et al. Spectral Cloud cover, a, d, Ireland HM, CP 176 0.82 600 * 0.62 Remote sensing 13.3 * multispectral [113] absorption accuracy Synthetic Aperture Sward surface Ali et al. [123] Ireland HM 264 0.75 - - - Satellite sensing Lack of research radar height 1 a 1 1 c e 1 d f Measure = measurement parameter, Prediction = prediction parameter; HM = herbage mass (kg DM ha ); DM = dry matter (g kg ha , % ); CP = crude protein (g kg DM , %DM ); DMD = dry matter f f 2 I digestibility (%DM ); WSC = water soluble carbohydrates (%DM ); R = coefﬁcient of determination; Error = * RMSE, standard error, ** mean absolute error depending on study; ‘-‘ = denotes where data was not published as part of study. Agriculture 2021, 11, 600 18 of 38 10. Decision Support Systems for Grassland Measurement Decision support tools (DST) are becoming more frequently used by grassland farmers to optimise the end use of their grass measurement data for the purposes of herbage allocation and pasture management. A number of grassland management DSTs have been developed in Europe [124,125] and an increasing amount of grassland data is being stored on cloud computing platforms. PastureBase Ireland (PBI) is a DST that assists farmers in determining appropriate actions to be taken to optimise grassland management, mainly by processing uploaded pasture HM cover estimations to determine appropriate herbage allocations in accordance with on-farm growth rates [20]. One signiﬁcant advantage of DSTs, such as PBI, is that they can perform as national databases for research and innovation. PastureBase can capture data for a range of paddock management parameters from farms across Ireland, which can be used for regional research studies [126]. User collaboration by means of online discussion group portals is also enabled through PBI’s interface [24]. Recent data from PBI indicates that farmers using the system are utilising more grass than the Irish national average (8 t DM year ) and are growing between 11 and 15 t DM year [127]. Studies have utilised online DST databases to combine grassland management factors with measurement and meteorological data from local weather stations to forecast HM growth rates [128,129]. Romera et al. [130] utilised an algorithm to continuously train a model to simulate growth factors between measurement dates on New Zealand dairy pastures. These growth factor simulations were based on a combination of meteorological and grass measurement data. Herrmann et al. [131] combined N fertilization, defoliation frequency, grass species, and daily weather data to predict HM and CP on pastures in Germany. In the near future, on-farm sensor technologies could provide data on site-speciﬁc meteorological and soil conditions to increase HM prediction accuracy [69]. One limitation of the previously mentioned DSTs is that they are currently only capable of processing HM and sward height data, which are acquired using conventional measurement techniques. Scope for a holistic grass management DST that incorporates state of the art grass technologies, which can measure both pasture quantity and quality, has been identiﬁed [132]. GrassQ was a European wide project that aimed to develop a holistic precision grassland measurement and management system, which encompassed both ground based and remote sensing measurement technologies [133]. For new DSTs to be adopted for regular use by grassland farmers, they will need to ensure reduction in labour and return of investment. The GMOT, a prototype grass measurement optimisation tool developed by Murphy et al. [35], generated grass measurement protocols that were optimised for both precision and labour efﬁciency. The tool was capable of optimising measurement routes and simulating measurement error, which facilitated cost beneﬁt analysis to be conducted for each measurement protocol based on measured HM vs. estimated labour and error costs. Cost–beneﬁt analysis should be an integral part of the design of any future grass management support system to determine the efﬁcacy of investing in new measurement technologies at farm level [24]. 11. Current Challenges Relating to Precision Pasture Measurement Signiﬁcant challenges currently restricting the implementation of precision pasture measurement at farm level that have been highlighted in the reviewed literature include sward heterogeneity, labour, and perceived measurement value amongst farmers. The lack of validation, robustness, and high cost of state-of-the-art measurement technolo- gies are further challenges to the optimisation of pasture measurement. The high spatial and temporal variability of grazed pasture has represented a signiﬁcant hindrance to the precision of conventional grass measurement technologies. One perceived solution to over- come poor measurement precision relating to highly variable swards has been to increase measurement sampling rates and ultimately measurement labour. Measurement errors caused by sward heterogeneity, high labour cost, and the poor precision of conventional grass measurement methods have resulted in poor perceptions and low uptakes in grass Agriculture 2021, 11, 600 19 of 38 measurement amongst farmers. Some of the state-of-the-art technologies discussed in this paper have the potential to overcome these issues. However, a period of time is required for long term studies that have performed sufﬁcient validation of the proposed technologies to become established in the literature. A number of studies outlined in this review have indicated the detrimental effects that climate conditions, such as excessive cloud, wind, and rain, have on pasture sensing data. Additionally, the potential high cost of new grass measurement sensors will not alleviate the poor perception that some farmers have of the value of frequent grass measurement. 12. Future of Grassland Measurement Within the literature outlined in this review, it is evident that there is considerable scope for the development of grassland sensing techniques to increase measurement precision, pasture mapping capabilities, and labour efﬁciency. Considerable potential exists to develop holistic grass measurement systems including multi-sensor conﬁgurations, which incorporate the beneﬁts of a range of measurement technologies. Concurrently, the combination of new grassland sensing technologies with state-of-the-art modelling techniques should lead to more precise predictions of pasture parameters. This will enable the exploitation of a wide range of data sources, including measurement, management, and climate factors, which would be facilitated by online DSTs. Moreover, analysis of mixed species swards should be accounted for within the design and calibration of future grass measurement technologies. Regarding the new technologies discussed in this review, more detailed long-term studies that account for annual and seasonal sward variation are required. Furthermore, scope exists to automate grass measurement using either manned or unmanned vehicles and this would aid the promotion of precision grass measurement amongst farmers. More research is required regarding the optimisation of grass mea- surement protocols that account for spatial and temporal heterogeneity in pasture in line with the principles of PA. The development of such protocols should be applicable to both herbage quantity and quality measurement techniques. The adoption of new preci- sion grassland measurement technologies within pasture-based industries will only be justiﬁed if these technologies are proven to be signiﬁcantly more precise and practical than established methods. Detailed cost–beneﬁt analysis will be required to justify the implementation of new measurement technologies at farm level. Additionally, new mea- surement technologies will need to have minimal labour requirements, be easy to use, and adequate training will need to be provided to farmers to promote frequent measurement of pasture. This will further ensure that high resolution and accurate grassland data are regularly recorded. 13. Conclusions This review summarised the basic principles of optimal grassland management on temperate pastures and the requirement for more precise and efﬁcient measurement tech- nologies in line with the concept of PA. The development of more robust and rapid technologies to predict pasture quantity and quality would enable the optimisation of herbage allocation and utilisation. Subsequently, this would lead to increases in proﬁtabil- ity and reductions in emissions within pasture-based systems. The main ﬁndings from this review were: The dominant factors that need to be addressed with regard to the development of pre- cision grassland measurement technologies are sward heterogeneity and measurement labour and cost There are no established technologies for determining real-time in-situ pasture qual- ity. The development of such technologies is vital for a more precise management of pasture. The development and integration of holistic grassland management and measurement systems is necessary to achieve precision grassland management. Agriculture 2021, 11, 600 20 of 38 Author Contributions: Conceptualization, D.J.M., M.D.M., B.O., M.O.; methodology, D.J.M., M.D.M., B.O., M.O.; software, D.J.M.; validation, M.D.M., B.O. and M.O.; formal analysis, D.J.M.; investigation, D.J.M.; resources, M.D.M., B.O., M.O.; data curation, D.J.M.; writing—original draft preparation, D.J.M.; writing—review and editing, B.O., M.D.M., M.O.D.; visualization, D.J.M.; supervision, D.J.M.; project administration, M.D.M., B.O.; funding acquisition, M.D.M., B.O. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the the ICT–AGRI GrassQ project (grant number 35779) and the Irish Department of Agriculture, Food and the Marine and the European Commission’s ERA-NET, ICT–AGRI scheme as part of the Horizon 2020 programme. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: This research was supported by the the ICT–AGRI GrassQ project (grant number 35779) and the Irish Department of Agriculture, Food and the Marine and the European Commission’s ERA-NET, ICT–AGRI scheme as part of the Horizon 2020 programme. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A Table A1. Summary of literature review dataset of studies relevant to grass measurement on temperate (Irish) grassland. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Grassland sward heterogeneity Sampling strategies for mapping “within-ﬁeld” Develop a variability in the Jordan protocol to Herbage 2003 dry matter yield Ireland PRG 1 Paddock et al. [34] measure and cuts and mineral map DM nutrient status of forage grass crops in cool temperate climes Correcting fresh grass allowance for Measure the rejected patches Klootwijk extent of rejected The Nether- 2019 due to excreta in PRG RPM 2 Paddock et al. [9] patches within lands intensive grazing pasture systems for dairy cows Frequency Measure the distributions of range and Barthram 2005 sward height distribution of Scotland PRG/mixed Sward stick 2 Paddock et al. [8] under sheep grass height grazing within pasture Variation in composition of Measure the Wilkinson pre-grazed pasture variation of grass 2014 UK Mixed NIRS 7 Paddock et al. [23] herbage in the quality in UK United Kingdom, pasture 2006–2012 Conventional grass measurement systems Agriculture 2021, 11, 600 21 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Critical analysis Herbage Techniques for of conventional cuts, RPM, Cayley & 1996 measuring pasture Australia - capacitance - Paddock Bird [43] pastures measurement meter, techniques sward stick The effect of intensive grazing Investigate the systems on the Klootwijk effect of grazing The Nether- 2019 rising plate meter PRG RPM 2 Paddock et al. [28] systems on RPM lands calibration for calibration perennial ryegrass pastures A comparison of Comparison of Visual methods used to conventional Nova Martin estimation, 2005 determine biomass pasture Scotia, Mixed 1 Paddock et al. [42] sward stick, on naturalized measurement Canada RPM swards methods Visual Comparison of Measuring estimation, conventional Mannetje biomass of The Nether- sward stick, 2000 pasture - - Paddock [44] grassland lands RPM, measurement vegetation remote methods sensing Factors inﬂuencing the accuracy of Calibration of Thomson herbage mass New Capacitance 1983 capacitance Mixed 2 Paddock [45] determinations Zealand meter meter with a capacitance meter Evaluation and calibration of an Earle & automated rising Mc Calibration of Victoria, 1979 plate meter for PRG RPM 2 Paddock Gowan RPM Australia estimating dry [46] matter yield of pasture Seasonal variation Ferraro in the rising plate Calibration of 2002 Ohio, USA Mixed RPM 3 Paddock et al. [47] meter calibration RPM for forage mass O’ Calibration of Visual assessment Visual Donovan 2002 visual Ireland PRG 2 Paddock of herbage mass assessment et al. [48] assessment Visual Comparison of A comparison of estimation, O’ conventional four methods of sward stick, Donovan 2002 pasture Ireland PRG 2 Paddock herbage mass RPM, et al. [26] measurement estimation capacitance methods meter The visual Calibration of Campbell Western, Visual 1973 assessment of visual Mixed 1 Paddock [49] Australia assessment pasture yield assessment Agriculture 2021, 11, 600 22 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Evaluation of Assessment of techniques for double sampling estimating the herbage technique Stockdale yield of irrigated Victoria, cuts and 1984 involving PRG/WC/mixed 1 Paddock [50] pastures Australia visual herbage cuts and intensively grazed assessment visual by dairy cows 1. assessment Visual assessment Visual Estimation of Comparison of L’Huillier estimation, herbage mass in conventional & New sward stick, 1988 ryegrass/white pasture PRG/WC 2 Paddock Thomson Zealand RPM, clover dairy measurement [51] capacitance pastures methods meter Investigate causes of Estimation of dairy Visual Thomson variation in New 1997 pastures-the need PRG/WC assessment, 2 Paddock et al. [52] pasture Zealand for standardisation RPM measurement across regions Practical use of the Assess the rising plate meter Visual Lile et al. measurement New 2001 (RPM) on New PRG/WC assessment, 3 Paddock [53] precision of the Zealand Zealand dairy RPM RPM farms Development of The Value of a double O’ Pasture Height in sampling Herbage Sullivan 1987 the Measurement Ireland PRG 1 Paddock technique for cuts, RPM et al. [54] of Dry Matter measuring Yield pasture Micro-sonic sensor technology enables Development of McSweeney enhanced grass GPS enabled 2019 Ireland - RPM 1 - et al. [55] height rising plate measurement by a meter Rising Plate Meter Greater understanding the density of grass to calculate the Calibration of Defrance 2004 growth and rising plate France PRG/WC RPM 13 Paddock et al. [56] biomass of a plot meter and the stock of grass available on a farm Comparison of Calibration of ﬁve Holshof different rising The Nether- 2015 rising plate meters PRG RPM 1 Plots et al. [57] plate meter lands in the Netherlands models Estimating forage Comparison of mass with a Sward stick, conventional Sanderson commercial Eastern, RPM, 2001 pasture Mixed 2 Paddock et al. [27] capacitance meter, USA capacitance measurement rising plate meter meter methods and pasture ruler Agriculture 2021, 11, 600 23 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons A survey analysis of grassland dairy farming in Ireland, Investigate investigating grassland Creighton 2011 grassland management Ireland PRG - 1 Paddock et al. [21] management, practices in technology Ireland adoption and sward renewal Utilising grassland management and Calibration of climate data for rising plate Murphy more accurate meter using state 2021 Ireland PRG RPM 3 Paddock/Plots et al. [59] prediction of of the art herbage mass modelling using the rising techniques plate meter Review of advancement of Herbage Mannetje Advances in grassland science The Nether- cuts, 2002 - - Paddock [60] grassland science and lands remote measurement sensing techniques Regular estimates of herbage mass Investigate the Beukes can improve effect of grass New 2019 PRG - 1 Paddock et al. [22] proﬁtability of measurement on Zealand pasture-based farm proﬁtability dairy systems Pasture sampling techniques Evaluation of the precision of the Assessment of rising plate meter RPM Murphy for measuring measurement 2020 Ireland PRG/WC RPM 2 Paddock/Plot et al. [29] compressed sward precision and height on sampling heterogeneous protocol grassland swards A method for approximate Development of Visual on-farm estimation a double Nakagami assessment, 2016 of herbage mass by measurement Japan Mixed 1 Paddock [10] herbage using two method for cuts, RPM assessments per pasture pasture Understanding Tasmanian dairy Investigate farmer adoption of farmer behaviour pasture Hall et al. with regard the 2019 management Tasmania - - 1 Paddock [64] adoption of grass practices: A measurement Theory of Planned technology Behaviour approach Developing an approach to assess Identify Eastwood farmer perceptions perceived value New 2020 - - 1 Paddock et al. [65] of the value of of grass Zealand pasture assessment measurement technologies Agriculture 2021, 11, 600 24 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons A protocol for Develop a grass Hutchinson New RPM, 2016 sampling pastures measurement Mixed 3 Paddock [66] Zealand C-DAX in hill country protocol Spatial variability Map and of soil properties Bernardi evaluate the Herbage 2016 and yield of a Brazil Alfalfa 1 Paddock et al. [68] spatial variation cuts grazed alfalfa of forage yield pasture in Brazil The role of precision agriculture in Review of the optimising soil potential for Higgins nutrient status and precision & Bailey 2017 grassland Ireland - - - Paddock agriculture in [69] productivity in grassland Northern Ireland, agriculture while reducing nutrient losses to air or water Measuring labour Quantiﬁcation of Herbage input on Deming labour input for cuts, RPM, 2018 pasture-based Ireland PRG 1 Paddock et al. [63] speciﬁc tasks on visual dairy farms using Irish dairy farms assessment a smartphone State of the art grass measurement systems Comparing methods to Togeiro estimate perennial Comparison of de RPM, hy- 2020 ryegrass biomass: RPM and remote Tasmania PRG 1 Plot Alckmin perspectral canopy height and sensing et al. [58] spectral vegetation indices Development of a grass Development of measurement a decision Murphy optimisation tool 2020 support tool to Ireland PRG RPM 3 Paddock/Plot et al. [35] to efﬁciently optimise grass measure herbage measurement mass on grazed pastures Practical Review of the Posudin spectroscopy in fundamentals of 2007 USA - NIRS - - [70] agriculture and agri- food science spectroscopy The use of NIRS to predict the Development of chemical de Boever NIRS for 1995 composition and Belgium - NIRS - - et al. [72] concentrate feed the energy value of quality analysis compound feeds for cattle Predicting Forage Development of Quality by NIRS for dried Norris 1976 Infrared and milled USA - NIRS - - et al. [73] Reﬂectance forage quality Spectroscopy analysis Agriculture 2021, 11, 600 25 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Predicting the dry matter intake of grazing dairy cows Development of Lahart 2019 using infrared NIRS to predict Ireland PRG/WC NIRS 3 Paddock et al. [74] reﬂectance dry matter intake spectroscopy analysis A Note on Development Estimation of NIRS Jafari Quality calibrations to 2003 Ireland PRG NIRS 2 Paddock/Plot et al. [75] Parameters in predict quality of Perennial Ryegrass dried and milled by near Infrared grass A note on the comparison of Development three near infrared NIRS PRG, reﬂectance Burns calibrations to Italian & 2014 spectroscopy Ireland NIRS 2 Plot et al. [76] predict quality of hybrid calibration dried and milled grass strategies for grass assessing herbage quality of ryegrass Assessment of Development herbage yield and NIRS quality traits of Burns calibrations to 2013 perennial Ireland PRG NIRS 3 Plot et al. [30] predict quality of ryegrasses from a dried and milled national variety grass evaluation scheme Effect of Development preparation NIRS Alomar method on calibrations to 2003 Chile Mixed NIRS 1 Paddock et al. [77] composition and predict quality of NIR spectra of dried and milled forage samples grass Near infrared technology for precision environmental Potential of NIRS McClure measurements: 2002 to analysis fresh Australia Fescue NIRS 1 Plot et al. [78] Part 1. grass N content Determination of nitrogen in green- and dry-grass tissue Effects of sample preparation and measurement standardization on Reddersen the NIRS Development in calibration quality NIRS to analysis Wachen- 2013 Germany Mixed NIRS 2 Plot of nitrogen, ash standing sward dorf and NDFom quality [79] content in extensive experimental grassland biomass Agriculture 2021, 11, 600 26 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Assessing the accuracy of current near infra-red reﬂectance Development of spectroscopy Thomson NIRS for 2018 analysis for fresh UK Mixed/WC NIRS 3 Paddock et al. [80] grass-clover grass-clover silage analysis mixture silages and development of new equations for this purpose Prediction of the composition of fresh pastures by Development of Alomar near infrared NIRS to analysis 2009 Chile Mixed NIRS 1 Paddock et al. [81] reﬂectance or fresh grass interactance- quality reﬂectance spectroscopy Impact of sampling and storage technique, Use of fresh and duration of grass NIRS to storage, on the analysis the Dale et al. 2017 composition of impact of sample Ireland PRG NIRS 1 Plot [82] fresh grass when storage and analysed using preparation near-infrared techniques reﬂectance spectroscopy Calibration models for the nutritional Development of quality of fresh Lobos NIRS to analysis 2019 pastures by Chile Mixed NIRS 2 Paddock et al. [83] fresh grass near-infrared quality reﬂectance spectroscopy A near infrared spectroscopy Development of Murphy calibration for the NIRS to analysis 2021 Ireland PRG NIRS 3 Paddock/Plot et al. [84] prediction of fresh fresh grass grass quality on quality Irish pastures Prediction performances of Evaluation of portable near Berzaghi maize silage Portable 2005 infrared Italy Maize 3 Paddock et al. [85] quality with NIRS instruments for at portable NIRS farm forage analysis A review on the applications of portable Review of the Teixeira 2013 near-infrared use of NIRS in Portugal - NIRS - - et al. [86] spectrometers in Agriculture the agro-food industry Agriculture 2021, 11, 600 27 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons NIR-Spectroscopy of non-dried forages as a tool in Development of Feuerstein breeding for higher portable NIRS to Portable & Paul 2007 Germany Mixed 6 Plot quality–laboratory analysis fresh NIRS [87] tests and online grass quality investigations on plot harvesters Use of portable NIRS equipment in Development of Mendarte ﬁeld conditions to portable NIRS to Basque Portable 2010 Mixed 1 Paddock et al. [88] determine the analysis fresh Country NIRS nutritional value of grass quality mountain pastures Machine learning algorithms to predict forage nutritive value of Development of Smith in situ perennial hyperspectral Victoria, 2020 PRG Hyperspectral 1 Plot et al. [89] ryegrass plants sensing for grass Australia using quality analysis hyperspectral canopy reﬂectance data The Use of Mobile Development of Near-Infrared Bell et al. portable NIRS to Portable 2018 Spectroscopy for UK Mixed/PRG/WC 1 Paddock [90] analysis fresh NIRS Real-Time Pasture grass quality Management Portable NIRS: a Assessment of novel technology Patton portable NIRS Portable 2018 for the prediction Ireland PRG 1 Paddock et al. [91] for fresh grass NIRS of forage nutritive quality analysis quality Comparison of Spectral Reﬂectance-Based Comparison of Smart Farming remote sensing Tools and a Portable Hart et al. and conventional 2020 Conventional Switzerland Mixed NIRS, Mul- 1 Plot [92] grass Approach to tispectral measurement Determine technologies Herbage Mass and Grass Quality on Farm Evaluating Use of Soil-Borne Causes multispectral of Biomass UAV and Multispectral, Vogel 2019 Variability in proximal sensing Germany Mixed proximal 1 Paddock et al. [93] Grassland by to evaluate sensing Remote and biomass Proximal Sensing variability The use of A multi-sensor hyperspectral approach for sensing and Ultrasound, Reddersen predicting biomass 2014 ultrasound to Germany Mixed Hyperspec- 2 Plot et al. [94] of extensively predict grass tral managed quality and grassland quantity Agriculture 2021, 11, 600 28 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Comparing mobile and static The use of a assessment of mobile Ultrasound, Safari biomass in muti-sensor unit 2016 Germany Mixed Hyperspec- 2 Paddock et al. [96] heterogeneous to measure grass tral grassland with a quantity and multi-sensor quality system Fusion of Ultrasonic and The use of Spectral Sensor hyperspectral Data for sensing and Ultrasound, Moeckel Improving the 2017 ultrasound to Germany Mixed Hyperspec- 1 Paddock et al. [97] Estimation of predict grass tral Biomass in quality and Grasslands with quantity Heterogeneous Sward Structure Development of Legg & Ultrasonic ultrasonic New Bradley 2019 Proximal Sensing sensors for rapid PRG Ultrasound 1 Plot Zealand [98] of Pasture Biomass measurement of grass height Calibration of the C-DAX Rapid Pasturemeter and Calibration of Rennie the rising plate the C-DAX to New 2009 PRG/WC C-DAX 1 Paddock et al. [99] meter for measure grass Zealand kikuyu-based quantity Northland dairy pastures Review of precision Lawrence Pasture agriculture tools New C-DAX, et al. 2007 Monitoring - - Paddock for pasture Zealand NIRS [100] Technologies measurement and mapping Pasture Mass Comparison of Estimation by the RPM, C-AX and King et al. C-DAX Pasture New 2010 herbage cuts for PRG/WC/Mixed C-DAX 1 Paddock [101] Meter: Regional Zealand grass Calibrations for measurement New Zealand Calibration of the Calibration of Oudshoorn C-DAX pasture C-DAX for grass et al. 2011 Denmark PRG/WC C-DAX 2 Plot meter in a Danish quantity [102] grazing system measurement Sward surface height estimation Comparison of Schori with a rising plate RPM and C-DAX et al. 2015 Switzerland Mixed C-DAX 4 Paddock meter and the for grass [103] C-Dax measurement Pasturemeter Development of Dennis Pasture yield measurement New et al. 2015 mapping: why & protocol for the - C-DAX 2 Paddock Zealand [104] how C-DAX to map pasture yield Agriculture 2021, 11, 600 29 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Introducing the Agri-Rover: An Manderson Automation of Autonomous New & Hunt 2013 C-DAX using - C-DAX - Paddock on-the-go sensing Zealand [105] robotics rover for science and farming Advanced pasture Development of Gobor management pasture care and et al. 2015 through innovative Germany Mixed Laser, NIRS 1 Paddock management [106] robotic pasture robots maintenance Comparison of Urban Lawn Marin remote and Monitoring in RGB et al. 2018 ground Spain - 1 Plot Smart City sensing [107] automated grass Environments measurement Proximal Soil Viscarra Sensing: An Calibration of Rossel Effective Approach proximal sensing France, 2011 - NIRS - - et al. for Soil techniques for Australia [109] Measurements in soil analysis Space and Time In-ﬁeld Calibration of hyperspectral Pullanagari hyperspectral proximal sensing New et al. 2012 sensing for grass Mixed Hyperspectral 1 Paddock for estimating Zealand [110] quality quality parameters measurement of mixed pasture Yield Estimates by Calibration of a Two-Step Ancin- proximal and Approach Using Murguzur satellite 2019 Hyperspectral Norway Mixed Hyperspectral 4 Paddock et al. hyperspectral Methods in [111] sensing for grass Grasslands at High measurement Latitudes Investigation of Pasture quality Pullanagari optical sensor for Multispectral, measurement tools New et al. 2011 the measurement - Hyperspec- 1 Paddock for decision Zealand [112] of pasture tral making quality Evaluation of Calibration of Grass Quality proximal and Askari under Different remote sensing Multispectral, et al. 2019 Soil Management methods for Ireland PRG/WC Hyperspec- 2 Paddock [113] Scenarios Using pasture quantity tral Remote Sensing and quality Techniques measurement Comparing UAV-Based Technologies and Evaluation of Rueda- RGB-D aerial and Ayala Reconstruction ground based RGB-Depth 2019 Norway Mixed 1 Paddock et al. Methods for Plant method for grass sensor [108] Height and quantity Biomass measurement Monitoring on Grass Ley Agriculture 2021, 11, 600 30 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Estimating Plant Traits of Statistical Grasslands from modelling Capolupo UAV-Acquired methods for et al. 2015 Hyperspectral hyperspectral Germany - Hyperspectral 1 Plot [114] Images: A grass Comparison of measurement Statistical data Approaches Proximal sensing of the seasonal Measuring the Pullanagari variability of variability of New et al. 2012 pasture nutritive pasture quality PRG/WC Multispectral 1 Paddock Zealand [115] value using using proximal multispectral sensing radiometry Machine learning estimators for the quantity and quality of grass Utilisation of Oliveira swards used for UAV sensing to RGB, Hy- et al. 2020 Finland Mixed 1 Paddock silage production measure silage perspectral [116] using drone-based grass quality imaging spectrometry and photogrammetry Portable LiDAR-Based Method for Development of Obanawa Improvement of LiDAR to Italian et al. 2020 Grass Height Japan LiDAR 1 Plot measure grass ryegrass [117] Measurement height Accuracy: Comparison with SfM Methods Review of 3D 3-D Imaging Vázquez- image Systems for 3-D Arellano technology for 2016 Agricultural Germany - imaging - Paddock et al. precision Applications—A systems [118] agriculture Review applications Examination of the potential of terrestrial laser scanning and Comparison of Cooper structure-from- LiDAR and RPM South Smooth LiDAR, et al. 2017 motion for grass Dakota, 1 Plot Brome RPM [119] photogrammetry quantity USA for rapid measurement non-destructive ﬁeld measurement of grass biomass Comparing the spectral settings of the new generation Comparison of broad and narrow proximal and Sibanda band sensors in Multispectral, satellite sensing South et al. 2016 estimating biomass Mixed Hyperspec- 1 Plot for grass Africa [120] of native grasses tral quantity grown under measurement different management practices Agriculture 2021, 11, 600 31 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Assessment of multi-temporal, multi-sensor radar and ancillary Calibration of Barrett spatial data for satellite radar for Satellite et al. 2014 Ireland PRG/WC - Paddock grasslands grassland radar [122] monitoring in classiﬁcation Ireland using machine learning approaches Application of Repeat-Pass TerraSAR-X Staring Spotlight Interferometric Calibration of Ali et al. Coherence to satellite radar for Satellite 2017 Ireland PRG 1 Paddock [123] Monitor Pasture grass quantity radar Biophysical measurement Parameters: Limitations and Sensitivity Analysis Grass measurement decision support systems PastureBase Development of Ireland: A grassland Rising plate Hanrahan grassland decision management meter, 2017 Ireland PRG 2 Paddock/Plot et al. [20] support system decision support Visual and national tool and national estimation database database Pastur ’Plan: a Introduction to a dynamic tool to decision support Delaby support grazing tool for et al. 2015 management France - RPM - Paddock grassland [124] decision making in measurement a rotational and management grazing system Development of GrazeVision: A Zom & a decision versatile grazing The Nether- Holshof 2011 support model - - - Paddock decision support lands [125] for grassland model management PastureBase Development of O’ Leary Ireland—getting grassland Rising plate & O’ Ireland utilising management meter, 2019 Ireland PRG - Paddock Donovan more grass. decision support Visual [127] Moorepark ’19 tool and national estimation Irish Dairy database The use of Weather forecasts McDonnell weather Grass to enhance an Irish et al. 2019 forecasting to Ireland PRG growth 4 Paddock grass growth [128] predict grass model model growth Agriculture 2021, 11, 600 32 of 38 Table A1. Cont. No. of Grass Measurement Trial Study Year Title Study Focus Region Grazing Species System Scale Seasons Development of the Moorepark St Gilles grass Development of Ruelle growth model Grass a grass growth et al. 2018 (MoSt GG model): Ireland PRG growth 2 Paddock model for Irish [129] A predictive model model pasture for grass growth for pasture based systems Use of a pasture growth model to Development of Romera estimate herbage Grass a grass growth New et al. 2010 mass at a paddock PRG growth 1 Paddock model for New Zealand [130] scale and assist model Zealand pasture management on dairy farms Performance of grassland under different cutting regimes as affected Calibration of Herrmann Grass by sward forage growth et al. 2005 Germany PRG/WC/mixed growth 3 Plot composition, and quality [131] model nitrogen input, soil model conditions and weather-A simulation study GrassQ-a holistic Development of precision grass decision support RPM, Hy- Murphy measurement and system to perspectral, et al. 2019 analysis system to process data Ireland PRG 2 Paddock multispec- [132] optimize pasture from multiple tral based livestock measurement production systems Modelling Development of precision grass decision support RPM, Hy- O’ Brien measurements for system to perspectral, et al. 2019 a web-based process data Ireland PRG 2 Paddock multispec- [133] decision platform from multiple tral to aid grassland measurement management systems DM = Dry matter, PRG = Perennial rye grass, WC = white clover, Paddock = predominately grazed pasture > 0.25 ha, Plots = simulated grazed plots <0.25 ha. 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Journal

Agriculture – Multidisciplinary Digital Publishing Institute

Published: Jun 28, 2021

Keywords: pasture-based agriculture; precision agriculture; remote sensing; spectroscopy; grass measurement; grassland sampling

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

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

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

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

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