Rising Plate Meter Research Articles

Crude Protein as an Indicator of Pasture Availability and Quality: A Validation of Two Complementary Sensors

This study evaluated the possibility of using two complementary electronic sensors (rising plate meter (RPM) and active optical sensor (AOS)) to obtain a global indicator, pasture crude protein (CP) in kg ha−1. This parameter simultaneously integrates two essential dimensions: pasture dry matter availability (dry matter (DM) in kg ha−1) measured by RPM, and pasture quality (measured by AOS), and supports management decisions, particularly those related to the stocking rates, supplementation, or rotation of animals between grazing parks. The experimental work was carried out on a dryland biodiverse and representative pasture, and consisted of sensor measurements, followed by the collection of a total of 144 pasture samples, distributed between three dates of the pasture vegetative cycle of 2023/2024 (Autumn—December 2023; Winter—February 2024; and Spring—May 2024). These samples were subjected to laboratory reference analysis to determine DM and CP. Sensor measurements (compressed height (HRPM) in the case of RPM, and normalized difference vegetation index (NDVI) in the case of AOS) and the results of reference laboratory analysis were used to develop prediction models. The best correlations between CP (kg ha−1) and “HRPM × NDVI” were obtained in the initial and intermediate phases of the cycle (autumn: R2 = 0.86 and LCC = 0.80; and Winter; R2 = 0.74 and LCC = 0.81). In the later phase of the cycle (spring), the accuracy of the forecasting model decreased dramatically (R2 = 0.28 and LCC = 0.42), a trend that accompanies the decrease in the pasture moisture content (PMC) and CP. The results of this study show not only the importance of extending the database to other pasture types in order to enhance the process of feed supplement determination, but also the potential for the research and development of proximal and remote sensing tools to support pasture monitoring and animal production management.

Estimation of Dry Matter Yield in Mediterranean Pastures: Comparative Study between Rising Plate Meter and Grassmaster II Probe

This study evaluates two expedient electronic sensors, a rising plate meter (RPM) and a “Grassmaster II” capacitance probe (GMII), to estimate pasture dry matter (DM, in kg ha−1). The sampling process consisted of sensor measurements, followed by pasture collection and a laboratory reference analysis. In this comparative study, carried out throughout the 2023/2024 pasture growing season, a total of 288 pasture samples were collected in two phases (calibration and validation). The calibration phase (n = 144) consisted of measurements on three dates (6 December 2023, 29 February and 10 May 2024) in 48 georeferenced sampling areas of the experimental field “Eco-SPAA” (“MG” field), located at Mitra farm (Évora, Portugal). This pasture is a permanent mixture of various botanical species (grasses, legumes, and others) grazed by sheep, and is representative of biodiverse dryland pastures. The validation phase (n = 144) was carried out between December 2023 and April 2024 in 18 field tests (each with eight pasture samples), in three types of representative pastures: the same mixture for grazing (“MG” field), a commercial and annual mixture for cutting (mowing) and conservation (“MM” field), and legumes for grazing (“LG” field). The best estimation model for DM was obtained based on measurements carried out in February in the case of the GMII probe (R2 = 0.61) and December 2023 and February 2024 in the case of RPM (R2 = 0.76). The estimation decreased very significantly for both sensors based on measurements carried out in May (spring). The validation phase showed greater accuracy (less RMSE) in “MG” field tests (RMSE of 735.4 kg ha−1 with GMII and 512.3 kg ha−1 with the RPM). The results open perspectives for other works that would allow the testing, calibration, and validation of these electronic sensors in a wider range of pasture production conditions, in order to improve their accuracy as decision-making support tools in pasture management.

PSXIII-5 Behavior and blood urea nitrogen in cattle grazing meadow bromegrass and supplemented with condensed and hydrolizable tannins

Abstract The nutritional value of grasslands declines as the season progresses, negatively impacting cattle nutrition. Protein supplementation may reverse this trend, although high rates of ruminal proteolysis reduce the efficiency of N utilization. Tannins decrease protein degradability in the rumen, although they are also plant defenses with potential to influence animal behavior. The objective of this study was to determine in protein-supplemented cows grazing meadow bromegrass (MB) the influence of tannins on blood urea nitrogen (BUN), and on behavioral levels of activity. Twenty-four Black Angus cow [body weight (BW) = 614 ± 20 kg]-calf (BW = 244 ± 4 Kg) pairs were allotted to six 9-acre paddocks (4 pairs/paddock); animals in the Control (Ctrl; n = 3) and Tannin treatments (TT; n = 3) grazed meadow bromegrass (MB) and had a supplement of dried distillers grains with solubles (DDGS; (1 kgּ cow-1ּ d-1), with the addition of a mix of condensed (2/3): hydrolizable tannins(1/3; SilvaTeam) at 0.4% of the diet for TT. Grazing occurred from July to September 2023 with four experimental periods of 15 d each, the first being the adaptation period (no tannins; Baseline). Data were analyzed as a mixed-effects model with repeated measures and paddock as the experimental unit. Cows were fitted with accelerometers on their left back leg to determine activity, and blood samples were collected from all animals at the end of each period to estimate BUN. Representative samples of grass and supplement were collected from each period and paddock and analyzed for CP and fiber content. Herbage availability (DM basis) in each paddock was assessed before and post-grazing during each period using a rising plate meter and calibration curves. neutral detergent fiber (NDF) (62.9 to 65.1 ± 0.5%) and and acid detergent fiber (ADF) (38.7 to 43.9 ± 0.3%) increased, whereas CP (6.8 to 3.9 ± 0.1%) declined from Period 1 to 4 (P < 0.05). Initial biomass was 5,272 ± 211 Kg/ha and it declined after grazing (Period 4) to 2,319 ± 94 Kg/ha (P < 0.05) with no differences between treatments (P > 0.05). No differences between treatments were observed on motion index, number of steps taken per day, and the proportional percentage of time cows spent standing (9,379 ± 740, 2,611 ± 300, and 54.9 ± 0.02%, respectively; P > 0.05), suggesting that cows in TT did not invest extra time in searching activities or resting relative to Ctrl. It was only detected a greater percentage of transitions from lying to standing in TT than in Ctrl (7 to 3.4 ± 0.003%; P < 0.001). Values for BUN did not differ between treatments (P > 0.05), although they declined from Baseline to Period 4 by 28 % (8.2 to 5.9 ± 0.2mg/dL) in TT and by 2.6 % (5.75 to 5.6 ± 0.01 mg/dL) in Ctrl (P < 0.05). These results suggest that the addition of low levels of tannins to a protein supplement in cows grazing a low-quality grass diet reduces ruminal proteolysis without adversely influencing biomass removal or animals’ levels of activity.

416 Implementation of virtual fencing in heifers for mountain summer grazing

Abstract A virtual fencing (VF) system is based on a smartphone application that defines an invisible grazing boundary based on global positioning. Animals are tracked via GPS collars. When reaching the virtual boundary, the collar emits an ascending audio tone, followed by a mild electric pulse (0.2 Joule) when crossing it. The technology is particularly promising in rough and extensive mountainous areas where physical fencing is difficult and more time-consuming. Environmental conditions may also affect the functionality of the VF system, with potential impacts on animal behavior. Therefore, the present study tested a VF system on 30 female heifers in a rotational grazing system on Swiss mountain pastures (approximately 1,300 to 1,500 m above sea level). Each heifer was equipped with a VF collar (Nofence AS, Batnfjordsør, Norway) for individual recording of audio tones and electric pulses. After two wk of VF training in the lowlands (approximately 700 m above sea level), during which the heifers learned to interpret the VF signals correctly, they were transported to the mountain pasture. For mountain grazing, the herd was divided into three groups of 10 heifers each, balanced for age (mean ± SD: 11.9 ± 1.6 mo) and breed (Holstein-Friesian, Montbeliarde, crossbreeds). The outer perimeter of the mountain pasture was surrounded by an electric fence. Its inner area was divided into 3 electrically fenced and 6 virtually fenced paddocks. For 83 d, all groups grazed simultaneously in separate paddocks and rotated sequentially through the 9 paddocks. Video cameras were placed along the virtual fences to record animal responses after receiving audio tones or electric pulses. Moreover, grass height was measured using a rising plate meter to estimate forage availability in the currently grazed paddocks. Throughout mountain grazing, each heifer received an average of 4.9 ± 6.9 audio tones and 0.3 ± 0.7 electric pulses per day. Generalized mixed-effects models revealed that the number of audio tones and electric pulses did not change over grazing periods or among days after paddock change. Behavioral responses of the animals were less pronounced once the heifers had learned the VF system. In addition, grazing interruptions after receiving an audio tone or electric pulse were shorter during mountain grazing compared with the training period (P < 0.001). Furthermore, lower grass heights (P < 0.05) as well as the occurrence of (common but) unpredictable events (P < 0.001), such as the presence of wildlife (e.g., lynx, deer) or neighboring cattle, increased the number of audio tones and electric pulses during mountain grazing. The VF system was still effective in keeping the heifers within their assigned paddocks under mountainous conditions. However, a well-considered handling when changing paddocks as well as a careful placement of the virtual fence is essential to avoid negative effects on the animals.

Accounting for minimum data required to train a machine learning model to accurately monitor Australian dairy pastures using remote sensing

Precision in grazing management is highly dependent on accurate pasture monitoring. Typically, this is often overlooked because existing approaches are labour-intensive, need calibration, and are commonly perceived as inaccurate. Machine-learning processes harnessing big data, including remote sensing, can offer a new era of decision-support tools (DST) for pasture monitoring. Its application on-farm remains poor because of a lack of evidence about its accuracy. This study aimed at evaluating and quantifying the minimum data required to train a machine-learning satellite-based DST focusing on accurate pasture biomass prediction using this approach. Management data from 14 farms in New South Wales, Australia and measured pasture biomass throughout 12 consecutive months using a calibrated rising plate meter (RPM) as well as pasture biomass estimated using a DST based on high temporal/spatial resolution satellite images were available. Data were balanced according to farm and week of each month and randomly allocated for model evaluation (20%) and for progressive training (80%) as follows: 25% training subset (1W: week 1 in each month); 50% (2W: week 1 and 3); 75% (3W: week 1, 3, and 4); and 100% (4W: week 1 to 4). Pasture biomass estimates using the DST across all training datasets were evaluated against a calibrated rising plate meter (RPM) using mean-absolute error (MAE, kg DM/ha) among other statistics. Tukey’s HSD test was used to determine the differences between MAE across all training datasets. Relative to the control (no training, MAE: 498 kg DM ha−1) 1W did not improve the prediction accuracy of the DST (P > 0.05). With the 2W training dataset, the MAE decreased to 342 kg DM ha−1 (P < 0.001), while for the other training datasets, MAE decreased marginally (P > 0.05). This study accounts for minimal training data for a machine-learning DST to monitor pastures from satellites with comparable accuracy to a calibrated RPM which is considered the ‘gold standard’ for pasture biomass monitoring.

Comparing grass biomass estimation methods for management decisions in a semi‐arid landscape

AbstractAimsEnvironmental managers require reliable and cost‐efficient monitoring methods for effective decision‐making. Understanding forage availability is important for managing wild, vertebrate herbivore populations. We developed a process for exploring the accuracy and cost efficiency of various biomass estimation techniques for a case study where semi‐arid woodland restoration is threatened by kangaroo grazing, with the aim of determining which method was most fit for purpose in a given decision context.LocationWyperfeld National Park, southeastern Australia.MethodsGrass biomass was estimated using a variety of methods, then compared to clipped biomass using linear models. Biomass estimation methods were either field‐based (i.e., rising plate meter, multispectral radiometer) or satellite‐based (i.e., Landsat satellite imagery, AussieGRASS forage production model). Sampling occurred across open and wooded semi‐arid vegetation types. We compared methods based on accuracy, the ability of each method to accurately predict a ‘forage‐switch’ threshold, cost, and the suitability for the management context.ResultsFor this case study, the multispectral radiometer was the most precise, yet most expensive, biomass estimation method over a single survey. However, satellite imagery proved to be the most cost‐efficient and fit for purpose, as it was inexpensive and most accurately estimated biomass around a forage‐switch threshold, second only to the multispectral radiometer. Accuracy of all methods was improved by including tree cover in the regression models.ConclusionsWe demonstrate a process for exploring which biomass estimation tool might be preferred for a given decision context, highlighting accuracy, consideration of tolerance to uncertainty and risk, the spatial and temporal scale of information required, and budget constraints.

PSIV-A-2 Comparison of rising plate meter and total collection methods for forage mass determination of tall fescue

Abstract The objective was to compare estimates of forage mass (FM) determined with a rising plate meter (RPM) with forage mass determined by total collection method. Estimates of FM impact management decisions and give the producer a greater understanding of forage availability for their herd. Traditional methods of FM estimation using a forage ring or quadrat and total collection are labor intensive and time consuming. The RPM tool aims to provide a quicker method of FM estimation without sacrificing accuracy. The reliability of this tool can be impacted by season, species composition, plant growth state, and pasture management. Forage samples were collected from six random locations within both continuous and rotationally grazed systems. The predominant forage species was endophyte-infected tall fescue. A 0.25 m2 forage ring was randomly thrown, then two RPM (Jenquip Manual Platemeter) measurements were collected in the ring, followed by five measurements adjacent to the ring. The RPM determined the compressed sward height (CSH). Subsequently, forage within the ring was hand-clipped at ground level and collected. Samples were dried for 120 h at 55° C to determine forage availability (kg/ha). This process was repeated weekly for 6 wk. Rising plate meter FM was estimated using the equation: kg dry matter (DM)/ha = CSH × 158 + 200. Data were analyzed using the CORR procedure of SAS 9.4. Forage mass estimates from total ring collection method and RPM were weakly correlated in the rotational grazing system (r = 0.26; P = 0.13) and in the continuous grazing system (r = 0.20; P = 0.24). It is important to note that certain sample collection dates had stronger correlations than others. In the rotationally grazed paddocks, FM from RPM were strongly correlated (r ≥ 0.89, P ≤ 0.02) with FM from total ring collection on 6/23/23 and 7/26/23. In the continuously grazed paddocks, FM estimations from RPM were strongly correlated (r ≥ 0.73, P ≤ 0.10) with forage mass from total collection on 7/20/23 and 7/26/23. Variation among the seven RPM readings at each collection site could have contributed to variability. Coefficient of variation ranged from 9 to 48% and averaged 18% for all samples. The inconsistencies in relationship of forage mass determination methods suggests that more variables need to be considered when determining the accuracy of RPM-derived estimates of forage mass of tall fescue.

Development of a Robust Sensor Calibration for a Commercially Available Rising Platemeter to Estimate Herbage Mass on Temperate Seminatural Pastures.

Rising platemeters are commonly used in Ireland and New Zealand for managing intensive pastures. To assess the applicability of a commercial rising platemeter operating with a microsonic sensor to estimate herbage mass with its own equation, the objectives were (i) to validate the original equation; (ii) to identify possible factors hampering its accuracy and precision; and (iii) to develop a new equation for heterogeneous swards. A comprehensive dataset (n = 1511) was compiled on the pastures of dairy farms. Compressed sward heights were measured by the rising platemeter. Herbage mass was harvested to determine reference herbage availability. The adequacy of estimating herbage mass was assessed using root mean squared error (RMSE) and mean bias. As the adequacy of the original equation was low, a new equation was developed using multiple regression models. The mean bias and the RMSE for the new equation were overall low with 201 kg dry matter/ha and 34.6%, but it tended to overestimate herbage availability at herbage mass < 500 kg dry matter/ha and underestimate it at >2500 kg dry matter/ha. Still, the newly developed equation for the microsonic sensor-based rising platemeter allows for accurate and precise estimation of available herbage mass on pastures.

Calculating herbage utilization and intake by dairy cows under subtropical conditions using conventional field measurement techniques or the HerbValo method.

This study aimed to evaluate the HerbValo method in comparison to conventional field measurement techniques as a tool for estimating the herbage utilization and dry matter (DM) intake by grazing dairy cows under subtropical conditions. It was carried out during 18 months in a commercial farm of Southern Brazil. The herbage utilization was estimated monthly in two to four randomly selected paddocks (total n = 40) using the rising-plate meter or the double-sampling technique. In parallel, the herbage utilization was estimated by the HerbValo method, which is based on simple descriptions of the herd, supplements, pasture and grazing management. Values of herbage utilization estimated by field techniques were linearly (P < 0.01) related to those estimated by HerbValo, with no effect of pasture type (tropical vs. temperate) on the origin or on the slope of the regression (slope = 0.97; origin = -0.1; R2 = 0.81; rsd = 0.17 t DM/ha). At cow × day level, values of herbage intake estimated by field techniques were also linearly related to those estimated by HerbValo (P < 0.01; R2 = 0.82; rsd = 1.30 kg DM/cow/day). A negative linear relationship (P < 0.01) between herbage and supplement intake was obtained for both field (slope = -1.06; R2 = 0.72; rsd = 1.64) and HerbValo (slope = -0.92; R2 = 0.82; rsd = 1.08) approaches. Herbage utilization and intake by a dairy herd in a subtropical grazing-based system can be reliably estimated by the HerbValo method with the advantage of not requiring direct field pasture measurements.

PSI-A-3 Validation of Rising Plate Meter for Predicting Herbage Mass in Tall-Fescue Pastures

Abstract The objective was to compare herbage mass (HM) estimates using a rising plate meter (RPM) and hand-sampled total collections using a forage ring. Accurate measurement of HM can be used to budget available forage in grazing systems, particularly as grass is a dynamic resource. While accurate estimation of HM can be achieved through total forage collection obtained from clipped quadrants, this is labor-intensive and time-consuming. For optimal and informed management, grass needs to be measured quickly and reliably in relation to both accuracy and precision. An RPM can be used to estimate the HM of grasslands based on the compressed sward height (CSH). 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, season species composition, and grassland management regime. Forage samples and heights were obtained weekly from grazed paddocks, which were rotated daily, over a 10-week period from June 6 to August 2, 2022. Paddocks were predominately endophyte-infected tall fescue. Each week two paddocks were randomly selected for sampling. Across both paddocks a total of 9 hand-clipped samples were randomly collected using a 0.25 m2 forage ring. All the forage within the forage ring was collected and dried for 48 h at 55 °C to determine forage DM. A Jenquip Manual Platemeter was used to measure the CSH within the forage ring before clipping. Herbage mass was estimated using the rising plate meter equation (kg DM/hectare = rising plate meter height × 158 + 200). The data were analyzed using CORR procedure of SAS 9.4. Herbage mass measured by total ring collection and RPM were not correlated (r = 0.15; P = 0.17). When RPM and total ring collection forage mass were averaged by pasture, they tended to be correlated (r = 0.44; P = 0.07). When the outlier weeks (samples 1 ,5, 6, and 7) were removed, HM measured by the total ring collection was correlated (r = 0.41; P = 0.01) to RPM. The variability in correlations across pastures and time suggest that further work is needed to understand how variables like season, weather, and clipping procedures affect reliability of RPM to accurately predict forage mass.

Field Evaluation of a Rising Plate Meter to Estimate Herbage Mass in Austrian Pastures.

Pasture management is an important topic for dairy farms with grazing systems. Herbage mass (HM) is a key measure, and estimations of HM content in pastures allow for informed decisions in pasture management. A common method of estimating the HM content in pastures requires manually collected grass samples, which are subjected to laboratory analysis to determine the dry matter (DM) content. However, in recent years, new methods have emerged that generate digital data and aim to expedite, facilitate and improve the measurement of HM. This study aimed to evaluate the accuracy of a rising plate meter (RPM) tool in a practical setting to estimate HM in Austrian pastures. With this study, we also attempted to answer whether the tool is ready for use by farmers with its default settings. This study was conducted on the teaching and research farm of the University of Veterinary Medicine in Vienna, Austria. Data were collected from May to October 2021 in five different pastures. To evaluate the accuracy of the RPM tool, grass samples were collected and dried in an oven to extract their DM and calculate the HM. The HM obtained from the grass samples was used as the gold standard for this study. In total, 3796 RPM measurements and 203 grass samples yielding 49 measurement points were used for the evaluation of the RPM tool. Despite the differences in pasture composition, the averaged HM from the RPM tool showed a strong correlation with the gold standard (R2 = 0.73, rp = 0.86, RMSE = 517.86, CV = 33.67%). However, the results may not be good enough to justify the use of the tool, because simulations in economic studies suggest that the error of prediction should be lower than 15%. Furthermore, in some pastures, the RPM obtained poor results, indicating an additional need for pasture-specific calibrations, which complicates the use of the RPM tool.

Comparison of Ground-Based, Unmanned Aerial Vehicles and Satellite Remote Sensing Technologies for Monitoring Pasture Biomass on Dairy Farms

Systematic measurement of pasture biomass (kg DM/ha) is crucial for optimising pasture utilisation and increasing dairy farm profitability. On-farm pasture monitoring can be conducted using various sensors, but calibrations are necessary to convert the measured variable into pasture biomass. In this study, we conducted three experiments in New South Wales (Australia) to evaluate the use of the rising plate meter (RPM), pasture reader (PR), unmanned aerial vehicles (UAV) and satellites as pasture monitoring tools. We tested various calibration methods that can improve the accuracy of the estimations and be implemented more easily on-farm. The results indicate that UAV and satellite-derived reflectance indices (e.g., Normalised Difference Vegetation Index) can be indirectly calibrated with height measurements obtained from an RPM or PR. Height measurements can be then converted into pasture biomass ideally by conducting site-specific sporadic calibrations cuts. For satellites, using the average of the entire paddock, root mean square error (RMSE) = 226 kg DM/ha for kikuyu (Pennisetum clandestinum Hochst. ex Chiov) and 347 kg DM/ha for ryegrass (Lolium multiflorum L.) is as effective as but easier than matching NDVI pixels with height measurement using a Global Navigation Satellite System (RMSE = 227 kg DM/ha for kikuyu and 406 kg DM/ha for ryegrass). For situations where no satellite images are available for the same date, the average of all images available within a range of up to four days from the day ground measurements were taken could be used (RMSE = 225 kg DM/ha for kikuyu and 402 kg DM/ha for ryegrass). These methodologies aim to develop more practical and easier-to-implement calibrations to improve the accuracy of the predictive models in commercial farms. However, more research is still needed to test these hypotheses under extended periods, locations, and pasture species.

Estimation of grass biomass consumed by rabbits housed in movable paddocks

Biomass allowance is a key feature in pasture-based rabbit production systems. It conditions not only the stock density (rabbits/m²) and/or the number of grazing days, it also influences the grazing behaviour of animals. When herbage restriction occurs, pelleted feed and/or cereal intake goes up. Inadequate pasture management may also impair the biomass quantity and quality if overgrazing occurs. To avoid the undesirable effects of overgrazing and better manage pellet and cereal intake, information on both biomass availability and rabbits’ grazing capacity are needed. Here, we present an adaptation of the rising plate meter method (developed for biomass intake measures for ruminants) for use in rabbit. To this end, we designed an experiment where two groups of 12 rabbits each were kept in two different fields: under an apple orchard (AO) or on fallow land (FL). We followed the animals for 5 consecutive weeks (from 45 to 80 d old). Rabbits lived in 25 m² movable paddocks, and every week a new paddock location (called paddock-spot) was made available for them. At each new paddock-spot, we measured the herbage height inside the paddocks and performed samplings of the available biomass (i.e. herbage cut after herbage height measurement) outside the paddocks. From this data we estimated the available biomass inside each paddock-spot by fitting linear regression equations of biomass to herbage height. Overall, rabbits in the AO and FL had access to 1328±65.7 and 1386±58.6 kg of dry matter (DM) per ha, respectively. In every field and paddock-spot, the biomass available was lower than the rabbits’ grazing capacity; overgrazing was the rule. Roughly, and under a restricted herbage allowance, rabbits in the AO ingested 45.2 g DM/d and rabbits in the FL 43.4 g DM/d. In the last week (64 to 80 d old), the biomass intake of rabbits in the AO and AL represented 26.4 and 23.5% of the total DM intake, respectively. These values, however, does not represent the real grazing capacity of growing rabbits. In this study, we provide some advice on the sampling method to obtain reliable biomass estimations and we mention two methods for handling influential observations in linear regression.

The Influence of Rotational Length, along with Pre- and Post-Grazing Measures on Nutritional Composition of Pasture during Winter and Spring on New Zealand Dairy Farms.

Simple SummaryInconsistencies in the quality (nutritional composition) of pasture herbage can potentially affect the lactational performance of dairy cows. The nutritional composition of ryegrass–white clover pastures was grazed at different rotation lengths in New Zealand, on the hypothesis that longer rotations lengths would result in more mature swards, with lower quality herbage. Grazing rotation length, leaf regrowth stage at grazing, and pre-grazing DM yield during winter and spring had significant effects on the pasture herbage quality. In New Zealand, the length of the grazing rotation is adjusted based on the pasture availability, seasonal variations, and growth rate of different pastures. This information can be helpful for dairy farmers to manipulate pasture management to optimise the cows’ performance.The quality of ryegrass–clover pasture was investigated between August (winter: start of calving) and November (spring: end of breeding) on pasture-based dairy farms (>85% of total feed from pasture) that had short (n = 2, Farms A and B; winter ~30 days, spring ~20–25 days) or long (n = 2, Farms C and D; winter ~35 days, spring ~25–30 days) grazing rotations to determine whether quality was affected by grazing rotation length (RT). Weekly assessments of pasture growth and herbage quality were made using a standardised electronic rising plate meter, and near-infrared spectroscopy, respectively. Data were subjected to repeated measure mixed model analysis, in which herbage quality was the outcome variable. The highest pre-grazing dry matter (PGDM) and height, post-grazing dry matter (DM) and height, and number of live leaves per tiller (leaf regrowth stage, LS) were present in late spring. Neutral detergent fibre (NDF), acid detergent fibre (ADF), metabolisable energy (ME), and organic matter digestibility (OMD) were positively correlated to each other (r2 ≥ 0.8) whilst ADF and lipid, and ADF and OMD were negatively correlated (r2 ≥ −0.8; p < 0.01). Metabolisable energy content was negatively correlated with ADF and NDF (r2 = −0.7, −0.8, respectively), and was inversely related to PGDM. Metabolisable energy was higher (p < 0.05) in farms with shorter (overall mean: 11.2 MJ/kg DM) than longer (10.9 MJ/kg DM) RT. Crude protein was also inversely related to PGDM and was higher with shorter (23.2% DM) than longer (18.3% DM; p < 0.05) RT. Pre-grazing DM affected the amount of pasture that was grazed and, hence, the amount of DM remaining after grazing (post-grazing DM or residual), so that PGDM was correlated with post-grazing height and residual DM (r2 = 0.88 and 0.51, respectively; both p < 0.001). In conclusion, RT, LS, and PGDM during winter and spring influenced the herbage quality, therefore, better management of pastures may enhance the productivity of dairy cows.

Sociotechnological Sustainability in Pasture Management: Labor Input and Optimization Potential of Smart Tools to Measure Herbage Mass and Quality

Investing labor time in herbage measurements is important for precision pasture management. In this study, the labor input of three smart herbage measurement tools—multispectral imagery linked to an unmanned aerial vehicle (UAV), a semi-automated rising plate meter (RPM), and near-infrared reflectance spectroscopy (NIRS) of cut herbage samples—and of direct observation was modeled based on the REFA work element method. Three to five users were observed during work execution to identify best-practice workflows. Time measurements were conducted using video footage. The resulting standard times of work elements were used to model labor input for herbage measurements in different farm sizes (i.e., milking platforms of 6–100 ha) and subdivisions of a farm’s milking platform (i.e., 4–45 paddocks). Labor time requirement differed between the smart farming tools (0.7–5.9 h) depending on the farm size and milking platform scenario. The labor time requirement increased for all tools with an increase in farm size and was lowest for the RPM. For the UAV tool, it did not increase noticeably when the division of the milking platform changed. Nevertheless, the potential to save time was identified for the UAV and the NIRS. Therefore, the automation of certain steps in the workflows would contribute to sociotechnological sustainable pasture management.

GROWTH ANALYSIS OF WHITE CLOVER (Trifolium repens L.) AND INDIRECT METHODS TO ESTIMATE ITS FORAGE YIELD

Dry matter (DM) accumulation and its relationship to some indicators of physiological efficiency on white clover (Trifolium repens L.) growth was studied to determine the optimal harvesting time by season. The experiment was carried out at the Colegio the Postgraduados Campus Montecillo, Texcoco, Mexico. Twenty-four plots, each measuring 3.7 X 1.7 m, were used, and distributed in a completely randomized design with eight treatments and three replicates. The treatments consisted of weekly successive cuts during an eight-week sprouting cycle during the middle of each season of the year. The evaluated variables were dry matter accumulation, crop growth rate (CGR), intercepted radiation (IR), and plant height (cm) estimated through the ruler and rising plate meter methods. Most herbage accumulation (p ≤ 0.05) occurred at the eighth week of the spring (2,688 kg DM ha-1). Most CGR (p ≤ 0.05) occurred at the first week of the summer (84 kg DM ha-1 day-1). The largest IR occurred at the sixth and ninth week of the summer (100 %). Maximum height occurred at week eight of the summer (p ≤ 0.05), which was 32 and 23.3 cm for the ruler and the rising plate meter methods, respectively. Dry matter yield increased linearly due to the favorable climatic conditions during spring-summer, where there was a higher intercepted radiation and higher crop growth. The ruler method showed a higher coefficient compared to the ascending plate, so the ruler method for white clover can be recommended.

PSI-5 Effects of Two Rotational Grazing Management Systems on Forage Quality, Forage Availability, and Cow Performance

Abstract The objective was to compare the effects of 2 rotational grazing systems on forage quality, forage availability, and cow performance. Multiparous, fall-calving beef cows (n = 360; BW = 613 ± 66 kg) were stratified by sire, age, body condition score (BCS), and body weight (BW), and allotted to 6 groups. Groups were randomly assigned to 1 of 2 treatments: 8-paddock system rotated every 5 d or 6-paddock system rotated every 7 d. Endophyte-infected tall fescue paddocks (6.1 ha) were grazed in 2 rounds of rotations from April to July. Cow BW and BCS were obtained on d 0, 28, 56, and 84. Forage heights were measured using a rising plate meter before and after grazing. Forage samples were clipped from each paddock prior to grazing for proximate analysis. Data were analyzed using the MIXED procedure of SAS 9.4. During round 1, forage from the 8-paddock system tended (P = 0.07) to have greater percent dry matter (DM), but no differences were detected (P ≥ 0.12) in organic matter (OM), neutral detergent fiber (NDF), acid detergent fiber (ADF), or crude protein (CP). During round 2, forage percent DM tended (P = 0.08) to be greater while CP tended (P = 0.10) to be lesser in the 8-paddock system. Additionally, the 8-paddock system had greater (P ≤ 0.05) forage NDF and ADF than the 6-paddock system, but OM was not different (P = 0.17) in round 2. However, grazing system did not affect (P ≥ 0.21) forage availability after grazing in either round. Cow BW and BCS were not affected (P ≥ 0.18) by grazing system on d 0, 28, 56, or 84. Despite minor differences in forage quality, rotational grazing management system did not impact forage availability or cow performance.

Multi-site calibration and validation of a wide-angle ultrasonic sensor and precise GPS to estimate pasture mass at the paddock scale

Ultrasonic sensors are a tool for pasture biomass measurement but there has been a lack of on-farm validation to prove their practicality and applicability. As a result, uptake of such sensors is still low. This study sought to perform a multi-site and multi-season calibration and validation of a wide-angle, vehicle-mounted, ultrasonic sensor gathering data while the unit was used in motion on seven commercial dairy farms. This was facilitated by the integration of the ultrasonic sensor, a Real-Time Kinematic Geographic Positioning System (RTK-GPS), and a datalogger into a single system that was attached via a custom bracket to the bull bar of an all-terrain vehicle. The resulting dataset was gathered over a 12-month period, and comprised 1886 reference biomass values from destructive cutting of perennial ryegrass pastures, accompanied by ultrasonic sensor height readings and compressed height readings from a rising plate meter (RPM): a tool that is widely used on commercial farms. The final root mean square errors of prediction (RMSEP) were 1034 and 904 kg DM/ha for Ultrasonic sensor and RPM respectively across all sites and seasons. These were considered high in comparison to previous literature, but this could be attributed to the very broad applicability of the models created. It was also demonstrated that the ultrasonic instrument could be used to create accurate biomass maps for paddocks in the same time taken to survey a whole paddock by RPM, a method that only results in a single paddock average value. Considering the ultrasonic sensor was only slightly less precise and accurate than the RPM at estimating biomass, but considerably more functional in its ability to determine within paddock biomass variation, it was concluded that the ultrasonic sensor was the more informative option.

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

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.

Exploring the potential of rising plate meter techniques to analyse ecosystem services from multi-species grasslands

Context Continuous grazing in extensive grassland creates grazed and ungrazed patches, which are important for ecosystem service delivery. One possibility for optimising ecosystem services is to target a defined compressed sward height, which can be measured with rising plate meters supplied with internal global navigation satellite system (GNSS) receivers. Aims We assessed the ability of a modern rising plate meter to map long-term patch structure in extensively grazed grassland as a means to improve estimation of phytodiversity at paddock scale as a paramount ecosystem service. Methods The patch class proportions obtained from interpolating the georeferenced compressed sward height values were used to calculate phytodiversity indicators at paddock scale as determined by dry-weight ranking. In addition, a modern rising plate meter with internal GNSS receiver was compared with an established one without GNSS receiver. Key results The mapping of the patch classes revealed decreasing proportions of tall patches with increasing stocking intensity. Phytodiversity at paddock scale depended on the proportions of respective patches, highlighting the importance of accurate mapping of patches for ecosystem service assessment. Conclusions The new rising plate meter extends the utilisation of compressed sward height measurements into a spatial context. Patch size, spatial distribution of patches within a paddock and spatial clustering of patches, as well as repeated mapping over time, can be utilised to detect change and monitor long-term management schemes without the need for sophisticated remote-sensing applications. Implications The combination of the new rising plate meter and dry-weight ranking extends grazing management towards biodiversity monitoring in an easy-to-learn approach.