Evaluation of the precision of the rising plate meter for measuring compressed sward height on heterogeneous grassland swards

Murphy, Darren; Brien, Bernadette O’; Hennessy, Deirdre; Hurley, MaryAnne; Murphy, Michael D.

doi:10.1007/s11119-020-09765-9

Cited by 20 publications

(17 citation statements)

References 45 publications

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“…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 relationships 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 −1 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.…”

Section: Pasture Sampling Techniquessupporting

confidence: 85%

“…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.…”

Section: Conventional Grass Measurementmentioning

confidence: 99%

“…There are a range of issues with these methods with regard to operator bias, precision, and difficulties in accounting for spatial variation [26][27][28]. Another significant issue concerning current grass measurement practices 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].…”

Section: Introductionmentioning

confidence: 99%

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

Murphy

O’Brien

et al. 2021

Agriculture

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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.

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Section: Pasture Sampling Techniquessupporting

confidence: 85%

Section: Conventional Grass Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Murphy

O’Brien

et al. 2021

Agriculture

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“…O' Donovan et al (2002) introduced the concept of grassland measurement using visual assessment to dairy farms in Ireland. The rising plate meter has been used successfully in Ireland and this technology has been improved through the incorporation of GPS and smartphone technology allowing it to be integrated with web-based grassland management tools (McSweeney et al, 2018;Murphy et al, 2021aMurphy et al, , 2021b.…”

Section: Grassland Decision Support Toolsmentioning

confidence: 99%

Irish Grassland Research — main achievements and advancements in the past 60 yrs and where to progress to next

O’Donovan

Dillon

Conaghan

et al. 2022

Irish Journal of Agricultural and Food Research

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In the last 60 yr Irish grassland production has increased substantially in no small part due to high-quality fundamental grassland research. Increased production from grassland has arisen from improved understanding (research and practice) of soil and plant nutrition, plant physiology and variety improvement, while improved understanding of feed evaluation, ruminant nutrition, grazing management and silage technology has contributed to increased utilisation of grassland. Annual grass DM production varies from 12.7 to 15.0 t DM/ha based on Department of Agriculture, Food and the Marine grass variety trials. More recent data from PastureBase Ireland indicate that average annual grass production (2020) on efficient dairy and dry stock farms is 13.5 and 10.0 t DM/ha, respectively. Ireland is now one of the world leaders in grassland research, particularly in the area of grazing utilisation, the development and use of grassland databases, decision support systems and grass selection indices for grass varieties. Future pasture-based systems must extend beyond food production to deliver additional benefits to farmers, to consumers and the wider society. Future systems will require more robust grazing animals with healthier functional traits, more diverse swards supporting improved animal performance and require fewer fertiliser and chemical inputs, and will support more biodiversity and enhanced carbon storage.

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“…With a known dry matter (DM) intake of cows, a precision grazing strategy can be pursued [ 8 ], and the timing of a pasture reallocation can be optimally chosen without wasting feed residues or using grasslands too intensively. However, measuring available herbage quantity by means of weekly farm walks using rising plate meters is laborious [ 9 , 10 ] and error-prone [ 11 ] and so are visual observations by humans. Measuring the available herbage quantity on pastures via unmanned aerial vehicles and satellites has been heavily investigated in recent years [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows

Hart

Dickhoefer

Paulenz

et al. 2022

Sensors

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In precision grazing, pasture allocation decisions are made continuously to ensure demand-based feed allowance and efficient grassland utilization. The aim of this study was to evaluate existing prediction models that determine feed scarcity based on changes in dairy cow behavior. During a practice-oriented experiment, two groups of 10 cows each grazed separate paddocks in half-days in six six-day grazing cycles. The allocated grazing areas provided 20% less feed than the total dry matter requirement of the animals for each entire grazing cycle. All cows were equipped with noseband sensors and pedometers to record their head, jaw, and leg activity. Eight behavioral variables were used to classify herbage sufficiency or scarcity using a generalized linear model and a random forest model. Both predictions were compared to two individual-animal and day-specific reference indicators for feed scarcity: reduced milk yields and rumen fill scores that undercut normal variation. The predictive performance of the models was low. The two behavioral variables “daily rumination chews” and “bite frequency” were confirmed as suitable predictors, the latter being particularly sensitive when new feed allocation is present in the grazing set-up within 24 h. Important aspects were identified to be considered if the modeling approach is to be followed up.

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Evaluation of the precision of the rising plate meter for measuring compressed sward height on heterogeneous grassland swards

Cited by 20 publications

References 45 publications

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

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

Irish Grassland Research — main achievements and advancements in the past 60 yrs and where to progress to next

Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows

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