# Dynamic algorithmic conversion of compressed sward height to dry matter yield by a rising plate meter

Diarmuid Mc Sweeney, Luc Delaby, Bernadette O'Brien, Alexis Ferard, Nicky

Byrne, Justin Mc Donagh, Stepan Ivanov, Neil Coughlan

► To cite this version:

Diarmuid Mc Sweeney, Luc Delaby, Bernadette O'Brien, Alexis Ferard, Nicky Byrne, et al. Dy-namic algorithmic conversion of compressed sward height to dry matter yield by a rising plate meter. Computers and Electronics in Agriculture, 2022, 196, pp.106919. 10.1016/j.compag.2022.106919. hal-03628017

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# Dynamic algorithmic conversion of compressed sward height to dry matter yield by a rising plate meter

Diarmuid McSweeney<sup>a, b</sup>, Luc Delaby<sup>c</sup>, Bernadette O'Brien<sup>a</sup>, Alexis Ferard<sup>d</sup>, Nicky Byrne<sup>a</sup>,
Justin McDonagh<sup>a</sup>, Stepan Ivanov<sup>b</sup>, Neil E. Coughlan<sup>e, *</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

Digital data capture

Grassland management

Grass measurement

Herbage mass

Dry matter yield

Rising plate meter

## ABSTRACT

The strategic allocation of pasture grazing area to dairy cows is essential for optimal management and increased outputs. Rising plate meters are frequently used to estimate pasture herbage mass, i.e. dry matter yield per hectare, by employing simple regression equations that relate compressed sward height to herbage mass. However, to improve the accuracy and precision of these equations, so that inherent variation of grasslands is captured, there is a need to incorporate differences in grass types and seasonal growth. Using a total of 308 grass plots, the variation of growth for both perennial ryegrass and hybrid ryegrass was recorded over the seven-month growing season, i.e. March–September. From these data three dynamic equations were derived. The models showed reduced levels of error in comparison to most other conventional equations. As such, the derived models represent a considerable advance for predictive assessment of herbage mass and will support more efficient grassland utilisation by farmers. Although all equations were found to be highly accurate and precise, only a single equation was considered the most effective (R² = 0.7; RMSE = 248.05), allowing herbage mass to be predicted reliably from compressed sward height data in relation to ryegrass type and calendar month. Although further research will be required, the results presented allow farm operators to calculate herbage mass, as well as support the development of decision support tools to improve on-farm grassland management, particularly at the local paddock rather than national level.

## 1. Introduction

There exists a substantial and growing demand for dairy products worldwide (Godfray et al. 2010), with global demand for milk expected to increase by 48% between 2005 and 2050 (Alexandratos & Bruinsma, 2012). In temperate climates, pasture-based ruminant production offers a competitive and sustainable alternative to intensive, high-input systems (Dillon et al. 2008; Lawrence et al. 2016; Delaby et al. 2020). In particular, the utilisation of grazed grass provides for a highly efficient, nutritious and inexpensive source of energy for ruminant production (Dillon et al. 2005; Finneran et al. 2012). Importantly, the quantity and quality of herbage offered to grazing animals has a substantial impact on their performance e.g. milk production (Patton et al. 2016). Accordingly, to meet the daily nutritional demands of animals, the strategic

allocation of grazing area is an essential management practice (e.g., O'Donovan, 2000; Kennedy et al., 2009; Curran et al., 2010). However, determination of the appropriate allocation of grazing area can only be achieved when using reliably accurate and precise estimates of herbage mass (HM; kg DM ha⁻¹), i.e. dry matter yield per hectare.

Accurate measurement of HM can also be used to budget available forage in grazing systems, particularly as grass is an unstable resource (Sanderson et al. 2001; López-Díaz et al. 2011). For example, regular estimation can help ensure an adequate supply of herbage to meet demand throughout the grazing season and inform decisions on the removal of surplus herbage to balance its supply and demand, whilst maintaining herbage quality. In addition, regular measurement of herbage can be used to identify poor performing grass swards, allowing the farmer to take corrective action such as reseeding, addressing soil

<footnote><sup>a</sup> Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co., Cork, Ireland</footnote>

<footnote><sup>b</sup> Telecommunications Software & Systems Group, Waterford Institute of Technology, Waterford, Ireland</footnote>

<footnote><sup>c</sup> INRA, AgroCampus Ouest, UMR 1348, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage, F-35590 Saint-Gilles, France</footnote>

<footnote><sup>d</sup> ARVALIS - Institut du végétal, Station Expérimentale de la Jaillière, 44370 La Chapelle Saint Sauveur, France</footnote>

<footnote><sup>e</sup> School of Biological, Earth & Environmental Sciences, University College Cork, Distillery Fields, North Mall, Cork, Ireland</footnote>

<footnote>* Corresponding authors.
E-mail address: neil.coughlan@ucc.ie (N.E. Coughlan).</footnote>
fertility issues, and drainage (O'Donovan 2000; Hakl et al. 2012; Shalloo et al. 2011). Considerable potential exists to increase the accuracy and precision of pasture allocation, and subsequent farm productivity (Creighton et al. 2011; Dillon 2011). In essence, greater use of reliably collected on-farm data can improve management practices, through the provision of knowledge-based real-time decision support tools.

While accurate estimation of HM can be achieved through assess-
ment of sward heights obtained from clipped quadrants, this is laborious
and time intensive endeavour (Sanderson et al. 2001; López-Díaz et al.
2011). Although HM is most often estimated by visual observation, this
method is highly subjective and prone to considerable inter-observer
variability (Tucker, 1980; O'Donovan et al. 2002; López-Díaz et al.
2011). For optimal and informed management, grass needs to be
measured quickly and reliably in relation to both accuracy and preci-
sion. The rising plate meter (RPM) can be used to estimate the HM of
grasslands based on the compressed sward height (CSH) (Sanderson
et al. 2001; Hakl et al. 2012). Overall, this device is considered to be an
accurate, precise and labour efficient method for sampling HM (Sand-
erson et al. 2001; Soder et al. 2006). However, device reliability can be
affected by the naturally large variation of dry matter (DM) within CSH,
which is governed by numerous factors, such as plant growth state
(Mosquera-Losada and Gonzalez-Rodriguez, 1998), season (Bransby
et al., 1977; Rayburn 2020), species composition (Castle 1976; Rayburn
2020), and grassland management regime (Powell, 1974).

In recent years, technological advances such as accurate sensors,
Global Navigation Satellite Systems (GNSS), Bluetooth connectivity, and
low-power portable user interfaces (i.e. smart-devices), have been used
to improve farm management practices (Dillon 2011). Accordingly,
these technologies can be used to improve in-field measurement and
facilitate real-time decision support in relation to grassland manage-
ment. In particular, an RPM utilising a micro-sonic sensor and digital
data capture *via* a Bluetooth communications link to a smart-device
application has been developed (i.e. Grasshopper, see McSweeney
et al. 2019). This RPM device and its associated micro-sonic sensor were
found to accurately measure sward height (McSweeney et al. 2019).
Although the device can be programmed to calculate HM within its
associated smart-device application using various formulas, a good
reference population to act as baseline data that has realistically
captured inherent variations of grassland is required for the develop-
ment of effective, reliable and dynamic algorithms.

To optimize reliability, equations need to be developed across the
growing season and for different grass species, ploidies and varieties.
Previously, for example, a dynamic formula was developed for North
West France on perennial ryegrass (*Lolium perenne L.*) monoculture
swards and mixed swards of perennial ryegrass and white clover
(Defrance et al. 2004). However, a significant effect of season was
observed within this formula, i.e. calculated HM based upon CSH varied
by month. Accordingly, optimal grassland management requires the use
of a formula altered on a monthly basis. Here, therefore, we develop a
dynamic formula to accurately determine HM for Irish temperate
grasslands throughout the grass growing season, for both perennial and
hybrid ryegrass.

## 2. Methods

2.1. Study site

The study was conducted upon perennial ryegrass and hybrid
ryegrass plots (n = 308) sown on a free-draining acid brown earth soil of
sandy loam texture at Teagasc, Animal & Grassland Research and
Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland (52°09′50″N,
08°15′50″W). Plots were managed under simulated (n = 120; 5 × 1.5 m)
or actual grazing (n = 188: 10 × 1.5 m) regimes. Plots managed under
simulated grazing conditions were mechanically harvested on eight to
nine occasions annually. While animal grazed plots were managed
equally on a 21–30 day grazing rotation resulting in eight to nine

sampling occasions annually.

Before sowing, glyphosate was used to kill the previous sward, the
entire area was then ploughed and tilled to provide a fine and firm seed
bed which received 37 kg N ha⁻¹, 37 kg P ha⁻¹ and 74 kg K ha⁻¹. All
plots were sown using a plot seeder (WINTERSTEIGER Plotseed S;
WINTERSTEIGER AG., Austria) in August. Once the newly sown plots
had reached the two leaf growth stage they were sprayed with a post-
emergence herbicide to control the establishment of broad-leaved
weeds. With an equal number of diploids and tetraploids, simulated
grazing plots were comprised of perennial ryegrass or hybrid ryegrass.
Both ryegrass types were established as monocultures at a sowing rate of
37 kg ha⁻¹, and as polycultures totalling 37 kg ha⁻¹, for all possible
combinations for sowing rates of: 9.25; 18.5; and 27.75 kg ha⁻¹. For
example, sowing rates were combined for perennial ryegrass (9.25 kg
ha⁻¹) and hybrid ryegrass (27.5 kg ha⁻¹), and again for the corre-
sponding mix of perennial ryegrass (27.5 kg ha⁻¹) and hybrid ryegrass
(9.25 kg ha⁻¹). Plots designated for actual grazing were likewise con-
structed using an equal number of diploid and tetraploid perennial
ryegrass types, with sowing rates of 34 and 37 kg ha⁻¹, respectively. All
actual grazing plots were sown as ryegrass monocultures.

All plots were constructed using a randomised complete block
design, consisting of four replicates. For a simulated grazing protocol,
plots were harvested using a rotary blade mower to a cutting height of 4
cm (Etesia Hydro 124D; Etesia Ltd., UK), when HM was visually esti-
mated as ~ 1500 kg DM ha⁻¹. Animal grazed plots were likewise
allowed to reach a visually estimated pre-grazing HM of ~ 1500 kg DM
ha⁻¹. The grazed area was offered on a replicate basis to dairy cows for
24–36 h, dependant on animal intake, to reach a target residual grass
height of ~ 4 cm. Before grazing, a 1 m² sub-sample was cut from actual
grazing plots following the procedure outlined for simulated plots.

## 2.2. Dry matter yield

Dry matter (DM) yield was determined by weighing all herbage cut
from simulated and actual grazing plots. Material from grazed plots was
then returned to the source plot to allow consumption by grazing cows.
In all cases, a 0.1 kg subsample was retained and dried at 60 °C for 48 h
to determine percentage DM content (% DM) in relation to original wet
weight. The HM was then derived with respect to the area cut, the wet
weight and the percentage DM content.

2.3. Grass height measurement

Ten CSH measurements were collected from each plot both imme-
diately before and post herbage removal. These measurements were
captured with a micro-sonic sensor unit (Grasshopper II; True North
Technologies, Ireland), mounted perpendicular to the shaft of a hand-
held, commercially available RPM (Jenquip; Filip's Manual Folding
Plate Meter, New Zealand). The Grasshopper micro-sonic sensor is
designed to measure the distance between the sensor and the top of the
rising plate, to determine height displacement of an object underneath
the plate. Instantaneous capture of height data, together with a geo-tag
describing the location, was facilitated via a Bluetooth communications
link between the sensor unit and an accompanying smart-device appli-
cation (Android operating system). All captured data was saved to the
smart-device in a comma separated (.CSV) format. Before data capture,
the micro-sonic sensor was normalised to ensure a baseline of height
zero is established while the plate was at its resting position.

## 2.4. Algorithm establishment

To establish an algorithm for the conversion of CSH to predicted HM,
a variety of variables were examined, including: type of ryegrass (TRG; 2
levels: perennial ryegrass and hybrid ryegrass); Month (7 levels: March –
September, inclusive); the percentage DM content (% DM); actual HM
(kg DM ha⁻¹); pre-cut CSH of grass (cm); height cut (cm), i.e. pre-cut
CSH minus the post-cut CSH; and DM per centimetre of grass cut, i.e. HM divided by height cut (kg DM cm⁻¹). Pre-cut CSH of < 5 cm were discarded, as were unrealistic values of > 550 kg DM cm⁻¹. Correlation coefficients were calculated for the examined variables to determine effect statistics (see Table 1). These coefficients were used to derive and validate values for the prediction of HM, in relation to actual values recorded for each plot. To assess model performance, Root Mean Square Error (RMSE) values were calculated for each equation, as well as the coefficient of determination (Pearson's R²) and relative prediction error (RPE). Model bias was calculated in relation to mean percentage error (MPE). These assessment criteria have been employed by similar prediction modelling studies (see Murphy et al. 2021, and citations therein), and will facilitate a comparative assessment of the results with those of other studies.

### 3. Results

In total, the constructed dataset was comprised of 1640 usable plot assessments, with each of these including values for all the required variables. First, a value for predicted HM was derived in relation to actual pre-cut CSH (h) values, and the pre-cut CSH square expression (h²). Within the equation, the corresponding coefficients were each multiplied by these selected parameters (Eq. (1): R² = 0.59; P < 0.001). All coefficients were highly significant at P < 0.001 (Table 1). RMSE of 291.21, RPE of 19.8% and MPE of -3.5% were calculated for Eq. (1):

$$ \text{Predicted herbage mass} = (-227.6 + (233.3 \times h) + (-5.35 \times h^2)) \quad (1) $$

Second, building on this approach, a predicted value for HM was derived using coefficients for TRG (t) and month (m), with inclusion of the actual pre-cut CSH (h) and the pre-cut CSH square expression (h²). Once again, the coefficients for both pre-cut CSH and the pre-cut CSH square expression were each multiplied by these model parameters (Eq. (2); R² = 0.7; P < 0.001). All coefficients were highly significant at P < 0.001 (Table 1). RMSE of 248.05, RPE of 17.9% and MPE of -3.3% were calculated for Eq. (2):

$$ \text{Predicted herbage mass} = (-446.5 + t + m + (263.9 \times h) + (-6.6 \times h^2)) \quad (2) $$

<caption>**Table 1**</caption>

Derived coefficients and associated F values (n = 1640). All P < 0.001, excepting the effect of percentage dry matter content (% DM) at P < 0.05.

<table><thead><tr><th></th><th>Eq. (1)</th><th>F</th><th>Eq. (2)</th><th>F</th><th>Eq. (3)</th><th>F</th></tr></thead><tbody><tr><td>Intercept</td><td>-227.6</td><td>-</td><td>-446.5</td><td>83.58</td><td>111.8</td><td>86.89</td></tr><tr><td>TRG</td><td>-</td><td>-</td><td>72.3</td><td>-</td><td>78.2</td><td>-</td></tr><tr><td>PRG</td><td>-</td><td>-</td><td>-72.3</td><td>-</td><td>-78.2</td><td>-</td></tr><tr><td>HRG</td><td>-</td><td>-</td><td>-</td><td>76.2</td><td>-</td><td>64.11</td></tr><tr><td>Month</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>March</td><td>-</td><td>-</td><td>90</td><td>-</td><td>-0.3</td><td>-</td></tr><tr><td>April</td><td>-</td><td>-</td><td>22.5</td><td>-</td><td>5.4</td><td>-</td></tr><tr><td>May</td><td>-</td><td>-</td><td>75.1</td><td>-</td><td>75.1</td><td>-</td></tr><tr><td>June</td><td>-</td><td>-</td><td>64.3</td><td>-</td><td>33.6</td><td>-</td></tr><tr><td>July</td><td>-</td><td>-</td><td>-275.9</td><td>-</td><td>-209.7</td><td>-</td></tr><tr><td>August</td><td>-</td><td>-</td><td>-160</td><td>-</td><td>-154.2</td><td>-</td></tr><tr><td>September</td><td>-</td><td>-</td><td>184</td><td>-</td><td>250.1</td><td>-</td></tr><tr><td>Pre-cut CSH</td><td>233.3</td><td>279.34</td><td>263.9</td><td>388.9</td><td>118.7</td><td>2133.01</td></tr><tr><td>Sq. Pre-cut CSH</td><td>-5.35</td><td>70.49</td><td>-6.6</td><td>120.67</td><td>-</td><td>-</td></tr><tr><td>% DM</td><td>-</td><td>-</td><td>-</td><td>-</td><td>8.9</td><td>6.48</td></tr><tr><td>R²</td><td>0.59</td><td>1170.54</td><td>0.7</td><td>428.09</td><td>0.68</td><td>388.35</td></tr><tr><td>Root Mean Square Error</td><td>291</td><td>-</td><td>248</td><td>-</td><td>256</td><td>-</td></tr><tr><td>RPE (%)</td><td>19.8</td><td>-</td><td>17.9</td><td>-</td><td>19.2</td><td>-</td></tr><tr><td>MPE (%)</td><td>-3.5</td><td>-</td><td>-3.3</td><td>-</td><td>-4.3</td><td>-</td></tr></tbody></table>

A third model for predicted HM was then developed using coefficients for TRG (t) and month (m), with inclusion of the percentage DM content (d) and the corresponding value for pre-cutting CSH (h). As before, the coefficients for the effects were each multiplied by their dependent model parameter (Eq. (3); R² = 0.68; P < 0.001). All coefficients were significant at P < 0.001, other than calculated percentage DM at P < 0.05 (Table 1). RMSE of 256.56, RPE of 19.2% and MPE of -4.3% were calculated for Eq. (3):

$$ \text{Predicted herbage mass} = (111.8 + t + m + (8.9 \times d) + (118.7 \times h)) \quad (3) $$

### 4. Discussion

This current study confirms the relationship between CSH and HM. Basically, the height of grass can be used as a reliable indicator of HM. Although Eq. (1) provides a simple straightforward estimate based on pre-cut CSH values alone, this equation cannot facilitate a dynamic assessment for type of ryegrass measured and time of year. Eq. (1) is also the least reliable given the associated Pearson's R², RMSE and RPE values. Both Eqs. (2) and (3) are especially beneficial as both can account for perennial ryegrass type and variation in relation to time of year. These equations will allow for the construction of dynamic formula within the smart-device application and associated novel micro-sonic RPM linked technology. The most applicable formula can be selected by an on-farm operator, based on the readily available information concerning the type of ryegrass and sampling month, to reliably predict HM. However, Eq. (2) is marginally more accurate and precise than Eq. (3), with respect to Pearson's R², RMSE, RPE and MPE values. Importantly, Eq. (2) is also a more advantageous formula, as it is derived from pre-cut CSH values rather than actual percentage DM content, which is not necessarily readily measurable on-farm because of impracticalities.

As demonstrated by many previous studies, it has been difficult to achieve RMSE values of below 250 kg DM ha⁻¹, with most studies achieving values closer to 300 kg DM ha⁻¹ (López-Díaz et al. 2011; Murphy et al. 2021). Although imprecise, RMSE values ranging from 250 to 300 kg DM ha⁻¹ represent the best available predictive equations for HM assessment based on measurements obtained from RPMs. Accordingly, the RMSE values obtained for all equations in this study are within an acceptable range, while both Eqs. (2) and (3) have especially favourable RMSE statistics. Interestingly, the inclusion of meteorological data in model calculations derived from machine learning techniques can provide for an improved RMSE value (243 kg DM ha⁻¹: Murphy et al. 2021). However, this is not considered to be a practical approach given the expense of on-farm meteorological sensors (see Murphy et al. 2021).

To date, most regression formulas used to calculate HM from CSH have been linear in nature, as this allows for easier calculations. However, smart polynomial regression formula, such as the equations derived by this study, are a far more accurate estimation of HM. For example, Michell and Large (1983) achieved strong correlations between CSH and HM (R² = 0.98) for specific time points across the grass growing season. However, when Sanderson et al. (2001) applied one of these time specific formulas consistently over a full grazing season, the correlation was significantly reduced (R² = 0.31). The additional model parameters required by Eq. (2), i.e. type of ryegrass and month, will be known to farm operators in the field and can, therefore, be selected as required. In general, all three derived equations displayed reduced error parameters (<20 % RPE, <5 % MPE) in comparison to most other models (≥24 % RPE, ≥9 % MPE; see Murphy et al. 2021, and citations therein). In particular, Eq. (2) considerably improves upon conventional models through reduced RPE and lower MPE values, and will advance the prediction of HM for pasture-based grazing systems in temperate zones.

Despite statistical indications of high accuracy and precision, further research will be required to better understand elements of formula
inaccuracy and imprecision. Therefore, an improved knowledge of on-farm variability is needed. To achieve this, additional model parameters could be included and validated, with a view to produce regional if not paddock specific formula, rather than national level equations. These equations could then be used to produce dynamic algorithms capable of calculating reliable HM estimates, based on operator selected criteria. With the advent of automated grass height data capture tools, such as micro-sonic RPMs and associated smart-device web-applications, dynamic and reliable calculation of HM can be achieved in a practical user-friendly manner. In addition, these tools can potentially be linked to other grassland technologies, to provide ‘smart-farm’ solutions through highly automated systems, including the deployment of virtual fences (see McSweeney et al. 2020). For example, upon collection of CSH data with a smart-device linked to an RPM with an on-board link to satellite navigation, a web based geolocation application can be used to define the optimal grazing area for the herd within a pasture. Here, we have produced a series of formulae that can be used within smart-device linked RPMs, for reliable algorithmic conversion of CSH to HM. Although further research is required to develop the equations to encompass more site-specific effects, our results represent a promising starting-point for the further advancement of decisions support tools, to improve on-farm grassland management.

## CRediT authorship contribution statement

**Diarmuid McSweeney:** Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing. **Luc Delaby:** Conceptualization, Formal analysis, Methodology, Validation, Writing – review & editing. **Bernadette O'Brien:** Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing. **Alexis Ferard:** Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing. **Nicky Byrne:** Data curation, Investigation, Resources, Writing – review & editing. **Justin McDonagh:** Data curation, Investigation, Resources, Writing – review & editing. **Stepan Ivanov:** Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing. **Neil E. Coughlan:** Data curation, Formal analysis, Investigation, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

The authors acknowledge support from FP7 Era-net of ICT GRAZINGTOOLS. In addition, we thank all staff at Teagasc Moorepark, Fermoy, particularly Caroline O'Sullivan. We also graciously thank two anonymous reviewers for helpful comments.

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