Article

Benefits of accurately allocating feed on a daily basis to dairy cows grazing pasture

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  • Norco co-operative Ltd
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Abstract

Two experiments were conducted, each over several months, when cows grazed either ryegrass (September-November 2001) or kikuyu (February-March 2002) pastures, to assess the effects of accurately allocating feed on a daily basis to lactating Holstein-Friesian dairy cows. In each case, 28 cows were randomly stratified into 2 equal groups on the basis of milk and milk component yield, liveweight, age and days in lactation. The metabolisable energy requirements of the animals were estimated from standard established requirements. In each experiment, both groups of cows received the same amount of supplement over a period that was equivalent to a pasture regrowth cycle of 12-16 days. The control group received a set amount of supplements each day, while supplements fed to the adjusted group varied, dependent on pasture available. Available pasture was varied from 7 to 21 kg DM/cow.day (above a stubble height of 5 cm), to mimic the variation found on well-managed dairy farms. When pasture available was above the predicted requirement for cows in the adjusted group, pasture availability was restricted to predicted requirements and the extra milk that could be produced from the spared pasture was estimated. However, cows in the control group had the opportunity to eat more pasture if allocated more than required. This could result in more milk being produced, a gain in liveweight, and/or a higher post-grazing pasture residue (and hence potentially improve pasture regrowth). If less pasture than required was allocated to the control group, production could reduce or the cows might graze harder. Thus, in the control group the proportion of forage to supplement remained relatively constant, but intake varied in relation to pasture allocated, while for the adjusted group the total intake was kept relatively constant. In experiment 1 (ryegrass), the milk yield, percentage of milk fat and liveweight change of cows in the control and adjusted groups was not significantly different. However, the cows in the adjusted group produced 0.016 kg/cow.day more milk protein. As the control group ate 0.35 kg DM/cow.day more ryegrass pasture (P = 0.008) it is assumed that accurate daily allocation of feed improved feed efficiency. In experiment 2, the milk yield and percentage of milk protein of cows grazing kikuyu pastures was not significantly different between groups but the percentage of milk fat and covariate-corrected liveweight at the end of the experiment was higher in the control group than in the adjusted group. The pasture spared by cows in the adjusted group was predicted to produce 8.9% more milk when grazing ryegrass pasture and 12.3% when grazing kikuyu pasture. Linear regression analysis of pasture on offer on post-grazing pasture residue was not significant for the cows in the adjusted group but was significant for the control group cows when grazing either pasture, indicating success in accurately allocating supplementary feed to maintain a constant grazing pressure. The results of this study should assist dairy farmers in deciding whether the effort required to allocate feed accurately to dairy cows on a daily basis, is worthwhile.

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... For each grazing event, the area allocated, the herbage mass in the selected paddock, and the number of animals in the mob determine herbage allowance per cow, which has important consequences for herbage DM intake and utilisation (Dalley et al. 1999). This in turn has consequences for defoliation severity (i.e. the postgrazing residual herbage mass), herbage regrowth, sward structure, quality of subsequent available herbage, and for supplement feeding and silage harvesting decisions (Fulkerson et al. 2005;Lee et al. 2007Lee et al. , 2008. ...
... However, as long as herbage allowance remains uncertain, adjustments to supplement feeding level will be arbitrary. Fulkerson et al. (2005) found that accurate daily herbage allocation to dairy cows resulted in a relatively constant daily intake, improved feed conversion efficiency, and consistent post-grazing residuals effected by maintaining a constant grazing pressure. They conclude that accurately allocating feed to dairy cows on a daily basis should significantly increase production potential of the pasture, with the major benefit of improving the use of pasture previously wasted by over-allocation (Fulkerson et al. 2005). ...
... Fulkerson et al. (2005) found that accurate daily herbage allocation to dairy cows resulted in a relatively constant daily intake, improved feed conversion efficiency, and consistent post-grazing residuals effected by maintaining a constant grazing pressure. They conclude that accurately allocating feed to dairy cows on a daily basis should significantly increase production potential of the pasture, with the major benefit of improving the use of pasture previously wasted by over-allocation (Fulkerson et al. 2005). ...
Article
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Paddock selection is an important component of grazing management and is based on either an estimate of herbage mass, or the interval since last grazing for each paddock. Obtaining estimates of herbage mass to guide grazing management can be a time consuming task. A value proposition could therefore assist farmers in deciding whether to invest resources in obtaining such information. A farm-scale simulation exercise was designed to estimate the effect of three levels of knowledge of individual paddock herbage mass on profitability of two typical pasture-based dairy systems in New Zealand; a medium input system stocked at 3.2 Friesian-Jersey cross bred cows/ha with ∼15% imported feed, and a high input system stocked at 4.5 Friesian cows/ha with ∼40% imported feed. The three levels of knowledge were: (1) 'perfect knowledge', where herbage mass per paddock is known with perfect accuracy, (2) 'imperfect knowledge', where herbage mass per paddock is estimated with an average error of 15%, (3) 'low knowledge', where herbage mass is not known, and paddocks are selected based on longest time since last grazing. In both systems, grazing management based on imperfect knowledge increased farm operating profit by ∼NZ$385/ha at a milk price of NZ$6.33/kg milksolids (fat + protein) compared with low knowledge. Perfect knowledge added a further NZ$155/ha to profit. The main driver of these results is the level of accuracy in daily feed allocation, which increases with improved knowledge of herbage availability. This allows feed supply and demand to be better matched, resulting in less incidence of under- and over-feeding, higher milk production, and more optimal post-grazing residual herbage mass to maximise herbage regrowth.
... Furthermore, pasture lands are arguably one of the primary and cheapest sources of livestock feed, particularly where agricultural enterprises are not feasible [2]. The profitability of a pasture-dairy based farm heavily depends on maximizing utilization of pastures, where feed availability for livestock can vary as widely as 50% [3][4][5]. The inherent spatial and temporal dependencies of pasture growth lead to high uncertainty in estimates for sward height data, especially when grasslands cannot be monitored with labor-intensive traditional methods. ...
... The inherent spatial and temporal dependencies of pasture growth lead to high uncertainty in estimates for sward height data, especially when grasslands cannot be monitored with labor-intensive traditional methods. This problem is essential as incorrect estimates result in wastage in areas with high forage availability and underfeeding of livestock at low forage availability [4]. Monitoring pasture growth with Unmanned Aerial Vehicles (UAVs) (e.g., [3]) and subsequently coupling with robot planning algorithms (e.g., [6][7][8][9][10][11][12][13]) can yield decisions for pasture feed allocation to maximize profitability. ...
... x t is calculated by fitting a linear curve between the available observations within its interval. Evaluation is done for (L in = L out = 15) with (δ = 32, s = 1) with MCMC inference, using 500 samples and p l = 0. 4 ...
Article
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Effective management of dairy farms requires an accurate prediction of pasture biomass. Generally, estimation of pasture biomass requires site-specific data, or often perfect world assumptions to model prediction systems when field measurements or other sensory inputs are unavailable. However, for small enterprises, regular measurements of site-specific data are often inconceivable. In this study, we approach the estimation of pasture biomass by predicting sward heights across the field. A convolution based sequential architecture is proposed for pasture height predictions using deep learning. We develop a process to create synthetic datasets that simulate the evolution of pasture growth over a period of 30 years. The deep learning based pasture prediction model (DeepPaSTL) is trained on this dataset while learning the spatiotemporal characteristics of pasture growth. The architecture purely learns from the trends in pasture growth through available spatial measurements and is agnostic to any site-specific data, or climatic conditions, such as temperature, precipitation, or soil condition. Our model performs within a 12% error margin even during the periods with the largest pasture growth dynamics. The study demonstrates the potential scalability of the architecture to predict any pasture size through a quantization approach during prediction. Results suggest that the DeepPaSTL model represents a useful tool for predicting pasture growth both for short and long horizon predictions, even with missing or irregular historical measurements.
... The foundation of a profitable pasture-based dairy farm is the maximum utilisation of the on-farm grown herbage as a source of high quality and low-cost feed (García et al., 2013;Beukes et al., 2019). Evidence from practical farm situations indicates that even under relatively uniform pastures, feed availability for cows can vary by up to 50% from daily intake requirements (Fulkerson et al., 2005;Insua et al., 2019b). The high uncertainty linked to the high temporal and spatial variability of pasture loads can lead to incorrect estimates of supplement feed requirements, resulting in concentrate wastage at high pasture availability or underfeeding the cows at low pasture availability (Fulkerson et al., 2005). ...
... Evidence from practical farm situations indicates that even under relatively uniform pastures, feed availability for cows can vary by up to 50% from daily intake requirements (Fulkerson et al., 2005;Insua et al., 2019b). The high uncertainty linked to the high temporal and spatial variability of pasture loads can lead to incorrect estimates of supplement feed requirements, resulting in concentrate wastage at high pasture availability or underfeeding the cows at low pasture availability (Fulkerson et al., 2005). The latter exposes pasture fields to a high grazing pressure leading to suboptimal post-grazing pasture residues reducing pasture regrowth (Fulkerson et al., 2005;Chapman et al., 2012). ...
... The high uncertainty linked to the high temporal and spatial variability of pasture loads can lead to incorrect estimates of supplement feed requirements, resulting in concentrate wastage at high pasture availability or underfeeding the cows at low pasture availability (Fulkerson et al., 2005). The latter exposes pasture fields to a high grazing pressure leading to suboptimal post-grazing pasture residues reducing pasture regrowth (Fulkerson et al., 2005;Chapman et al., 2012). Accurate daily estimates of pasture feed allocation are therefore critical to maximise the profitability of pasture-based dairy system (Romera et al., 2010;Beukes et al., 2019). ...
Article
Accurate daily estimates of pasture biomass can improve the profitability of pasture-based dairy system by optimising input of feed supplements and pasture utilisation. However, obtaining accurate pasture mass estimates is a laborious and time-consuming task. The aim of this study was to test the performance of an integrated method combining remote sensing imagery acquired with a multispectral camera mounted on an unmanned aerial vehicle (UAV), statistical models (generalised additive model, GAM) and machine learning algorithms (random forest, RF) implemented with publicly available data to predict future pasture biomass loads. This study showed that using observations of pasture growth along with environmental and pasture management variables enabled both models, GAM and RF to predict the pre-grazing pasture biomass production at field scale with an average error below 20%. If predictive variables (i.e. post-grazing pasture biomass) were excluded, model performance was reduced, generating errors up to 40%. The post-grazing biomass information at high spatial resolution (<1 m) acquired with the UAV-multispectral camera system was used as predictive variable for future pasture biomass. With the inclusion of the spatially explicit post-grazing biomass variable both models accurately predicted the pre-grazing pasture biomass with an error of 27.7% and 22.9% for RF and GAM, respectively. However, the GAM model performed better than RF in reproducing the spatial variability of pre-grazing pasture biomass. This study demonstrates the capability of statistical and machine learning models implemented with UAV or manually obtained pasture information along with publicly available data to accurately predict future pasture biomass at field and farm scale.
... In order to formulate supplements for grazing cows there needs to be information on the nutrient requirements of the cow and its ruminal microbes, the nutrient composition of the pasture consumed, the expected pasture intake and interactions between the pasture and the supplement (Kellaway et al., 1993;Paterson et al., 1994;Bargo et al., 2003a). In a pasture-based system, the supplement is accurately calculated after the pasture intake and quality are guessed (Fulkerson et al., 2005). There needs to be an understanding of the supply of pasture nutrients and the order in which nutrients limit milk production (Kolver & Muller, 1998). ...
... Many grazing studies (including McCormick et al., 2001a;Schor & Gagliostro, 2001;Bargo et al., 2001;Delahoy et al., 2003;Gehman et al., 2006 andSoder et al., 2006) (Bargo et al., 2002a;Gehman et al., 2006). Reeves et al. (1996), Dillon et al. (1997), Granzin (2004), Fulkerson et al. (2005) and Horan et al. (2006) estimated the intake of grazed grass by the cows using the n-alkane technique which uses the herbage C33 (or C31) to dosed C32 alkane ratio or C35 (high in kikuyu) to C36 alkane ratio (Reeves et al., 1996). This method relies on the recovery rate of the different alkanes being the same and is generally more accurate and precise than using the rising plate meter (RPM; see section 2.6.3.2; ...
... Indirect techniques for measuring pasture include visual assessment, a sward stick, the rising or falling plate meter and the electronic capacitance probe (Gourley & McGowan, 1991;Fulkerson & Slack, 1993;Tesfa et al., 1995;Fulkerson et al., 2005). The latter two are useful for obtaining herd estimates of pasture intake, are non-destructive and useful in overcoming errors from variability within paddocks since many measurements can be conveniently obtained (Earle & McGowan, 1979;Reeves et al., 1996). ...
... In this regard, there is a substantial opportunity for most commercial farms to lift profitability, given the large gap between current and potential pasture utilization [4][5][6]. Accurate measurement and allocation of pasture can increase milk production by ≈10%, mainly by utilizing the pasture that otherwise would be wasted [7]. However, the proportion of farms that conduct daily measurements of pasture for better grazing management decision making is small, and even fewer use or have any technology for this purpose [8]. ...
... Considering 30.3 Australian dollars (A$) per hour as imputed labor cost for the farmers' time [1], monitoring the whole farm every week could cost the farm somewhere between 91 and 182 A$/week (for 3-5.9 h, respectively). The additional labor costs could be offset by the ≈10% increase in milk production expected from systematic pasture monitoring [7], which would be around 1,605 A$/week for an average Australian dairy farm milking 273 cows, producing 17 L/cow.d and receiving a farmgate milk price of A$ 0.50/L [36]. ...
Article
Full-text available
There is a substantial opportunity to lift feed utilization and profitability on pasture-based dairy systems through both increased pasture monitoring accuracy and frequency. The first objective of this experiment was to determine the impact of the number of electronic rising plate meter (RPM) readings and walking pattern on the accuracy of the RPM to determine pasture biomass. The second objective was to evaluate current satellite technology (i.e., small CubeSats and traditional large satellites) in combination with the electronic RPM as an accurate tool for systematic pasture monitoring. The experiment was conducted from October to December 2019 at Camden, Australia. Two experimental paddocks, each of 1.1 ha, were sown with annual ryegrass and monitored with an electronic RPM integrated with Global Navigation Satellite System and with two different satellites (Planet CubeSats and Sentinel-2 satellite). Here we show that 70 RPM readings achieve a ± 5% error in the pasture biomass estimations (kg DM/ha), with no effect of the walking pattern on accuracy. The normalized difference vegetation index (NDVI) derived from satellites showed a good correlation with pasture biomass estimated using the electronic RPM (R2 0.74–0.94). Satellite pasture biomass and growth rate estimations were similar to RPM in one regrowth period but underestimated by ≈20% in the other. Our results also reveal that the accuracy of uncalibrated satellites (i.e., biomass estimated using NDVI to kg DM/ha standard equations) is low (R2 0.61, RMSE 566–1307 kg DM/ha). However, satellites calibrated with a RPM showed greater accuracy in the estimations (R2 0.72, RMSE 255 kg DM/ha). Current satellite technology, when used with the electronic RPM, has the potential to not only reduce the time required to monitor pasture biomass manually but provide finer scale measurements of pasture biomass within paddocks. Further work is required to test this hypothesis, both spatially and temporally.
... inaccurate allocation), because additional supplementary feed may need to be used to maintain animal performance and 'compensate' for this inaccurate allocation. Fulkerson et al. (2005) estimated that an additional 8.9% and 12.3% of milk yield per ha could be produced with the savings of perennial ryegrass or kikuyu pasture, respectively, in a control grazing study where accurate allocation was evaluated. ...
... Table 2. Summarised statistics (mean, standard error, and 25, 50 and 75% quartiles) of chemical fractions related to the nutritive value of kikuyu CP, Crude protein; NO 3 -N, nitrate nitrogen; ADIN, acid detergent insoluble nitrogen; DMD, dry matter digestibility; OMD, organic matter digestibility; NDFD, neutral detergent fibre digestibility; ND, nitrogen digestibility; ME, metabolisable energy; CF, crude fibre; ADF, NDF: acid, neutral detergent fibre; ADL, acid detergent lignin; WSC, water-soluble carbohydrate; NFC, non-fibre carbohydrate. Data from: Pearson et al. (1985); Hughes et al. (1988); Herrero et al. (1996); Manyawu and Madzudzo (1996); Reeves and Fulkerson 1996;Davison et al. (1997); Fulkerson et al. (1998Fulkerson et al. ( , 1999Fulkerson et al. ( , 2005Fulkerson et al. ( , 2006 Table 3. Summarised statistics (mean, standard error, and 25, 50 and 75% quartiles) of macro and micro minerals contents in kikuyu Ca, Calcium; P, phosphorous; K, potassium; Na, sodium; Mg, magnesium; S, sulfur; Cl, chlorine; Cu, copper; Zn, zinc; Mn, manganese; Fe, iron; OA, oxalic acid. Data from Pearson et al. (1985); Hughes et al. (1988); Reeves and Fulkerson (1996); Davison et al. (1997);Fulkerson et al. (1998Fulkerson et al. ( , 1999; Granzin (2003Granzin ( , 2004; Malleson et al. (2009) The substantial deficiency of Na is not unexpected. ...
Article
Full-text available
The amount of pasture grown and converted to animal product is closely linked with the profitability of pasture-based systems. Kikuyu (Pennisetum clandestinum Hochst. ex Chiov.) is the predominant C4 grass in coastal Australian beef and dairy systems. These kikuyu-based production systems face several key challenges to achieving high levels of productivity. In this review, we bring together the literature to highlight the opportunities for closing the gap between current and potential utilisation and for increasing dairy production from kikuyu-based pastures. More specifically, we highlight the significant gains that can be made on kikuyu-based commercial farms based on a conceptual model to show where the main losses originate, namely input and grazing management. The physical limitations associated with kikuyu for dairy systems are also presented, such as the relatively higher content of cell wall and lower content of water-soluble carbohydrates, together with nutrient imbalances relative to other grass species. Together, these limitations clearly indicate the need of supplying cows with supplements (particularly grain-based concentrates) to achieve moderate to high milk yield per cow. To achieve this without compromising pasture utilisation, dairy producers farming on kikuyu-based pastures need to use relatively greater stocking rates to generate enough demand of feed that can be used to align rate of pasture intake with rate of pasture growth, creating enough deficit of feed per cow to justify the addition of supplementary feed without impinging on pasture utilisation. The variability that exists between cows in kikuyu dry matter and neutral detergent fibre intake is also highlighted in this review, opening up new avenues of research that may allow significant productivity gains for kikuyu-based dairy farming in the future. Additional keywords: dry matter yield, grazing management, milk production, pasture allocation, pasture-based dairy, pasture utilisation.
... Additionally, supplying an appropriate and consistent amount of feed to cows is difficult within dynamic pasture-based grazing systems. However, Fulkerson et al. (2005) showed that accurate allocation of feed can result in approximately 10 percent higher milk yield. According to Fulkerson et al. (2005) in pasture based dairy farming supplements are often accurately calculated yet 'pasture intake and quality are guessed'. ...
... However, Fulkerson et al. (2005) showed that accurate allocation of feed can result in approximately 10 percent higher milk yield. According to Fulkerson et al. (2005) in pasture based dairy farming supplements are often accurately calculated yet 'pasture intake and quality are guessed'. In response, new tools have recently been developed targeted at achieving an improved fit with farm management systems through greater ease of use and reduced time commitment. ...
Conference Paper
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Australian dairy farms rely on grazing pastures as their primary and cheapest source of feed. Accurate and timely measurement of pasture biomass is integral for effective grazing management practice, however few Australian dairy farmers record pasture mass or growth rate objectively. A system using satellite images has been developed to measure pasture biomass at a paddock-scale in Australia. The concept was evaluated through an 18 farm pilot study over the spring growth period, July to November 2008. The study was evaluated in terms of technology fit with grazing management practice of participant farmers. Qualitative research methods, including semi-structured interviews and a group workshop, were used to ascertain participant views on issues such as timeliness, accuracy, and value proposition within the context of farming systems. In this paper we discuss preliminary findings from the study, focussing on the farmer attitudes to the use of satellite-based measurement and delivery of pasture biomass information. The findings suggest that a technology such as satellite pasture measurement has potential application in Australian dairy farm systems. However the provision of data alone does not guarantee successful technology uptake. Support structures must also be provided to help farmers interpret the information within the specific context of their farm system. These support structures may include use of private agronomists, producer groups, agriculture extension personnel, or associated software applications.
... When LS increases beyond the optimum, dying leaves increase fibre levels and this, along with advanced reproductive development, decrease the digestibility [33,34]. Therefore, harvesting a surplus of grass for conservation at peak growth is essential to maximise pasture utilisation and quality [36][37][38][39]. In agreement with results from studies by Fulkerson and Donaghy [34] and Turner et al. [40], the present study also found that as the LS increased above 3, both the ME and CP contents were lower. ...
Article
Full-text available
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.
... Given that the gains assumed here are unlikely to accrue every year due to (greater) seasonal variability, then 2% per year, a continuation of the recent historical trend, is probably not an unrealistic target. In the New Zealand example, productivity increases above that due to pasture breeding (Woodfield and Easton 2004) and better animal genetics (MacDonald et al. 2008) are assumed to come from: (1) the category 1 practice of full adoption of feed budgeting over 7 years from a current 20% of farmers bringing about 9% more milk production per cow (Fulkerson et al 2004), (2) a category 2 practice of " precision " feeding and milking tailored to individual cows giving 5% improvement when developed and adopted over 15 years, (3) a category 3 technology of high sugar grasses, developed and adopted over 20 years, which result in better conversion of protein to milk of 2%. Using these assumptions a weighted yield increase of 2.2% per year is projected. ...
Article
With increasing focus on global food security it is timely to examine the historical performance of Australian and New Zealand agriculture and assess future prospects. While Australia and New Zealand are minor contributors to world food production, they do contribute significantly to world wheat and dairy exports. In the last 40 years farmers in both countries have sustained linear growth in crop and livestock production per hectare. This has been driven by development and adoption of new technologies, specialization and higher use of inputs. At the same time there have been adjustments in industries towards economies of scale and substitution of labour with capital. Future productivity gains will rest with continuing improvement in per hectare production as in both countries the prospects for expansion in the area devoted to key commodities are limited (and in many regions declining). If future growth is to be sustained, it will need to be supported by effective R, D and E to both facilitate adoption of current technologies and develop new pathways for productivity improvement. For a range of reasons it is realistic to assume agriculture is moving into a phase where productivity growth will be driven by greater efficiency of use of fixed and variable inputs rather than an increase in input levels. This will occur against a background of climate change, which will place particular stress on industries limited by water supply. Introduction There has been a renewed public attention on global agricultural productivity in recent times due to concerns about food security, food prices, the financial viability of farm businesses under rising costs, and declining availability and affordability of critical inputs such as suitable land, labour, energy, water and fertiliser. This focus has also occurred in Australia and New Zealand whose agriculture sectors share many common features. Agricultural production in both countries is currently dominated by family owned and operated businesses, exporting much of their produce on world markets, without substantial government support programs and in a commercial environment where agriculture is a declining contributor to the national economy. Maintaining growth in agricultural productivity, achieved by technology development, on-farm adoption and increased scale, has been necessary to offset the decline in farmers' terms of trade (Mullen 2010). At the same time, systems of production have had to adapt to the growing environmental and animal welfare imperatives imposed by society, through government, on agriculture. Recent analyses have highlighted an apparent slowing in the rate of growth in agricultural production relative to impressive productivity gains over the last 30 years (Mullen 2007). This makes it timely to review recent progress in Australia and New Zealand and gauge prospects for future growth, particularly in the light of future technologies, climate change and government regulation. The aims of this paper are threefold: (1) summarise historical trends in agricultural production and constraints to further growth, (2) review the historical role of science and technology in sustaining innovation and productivity growth, and (3) analyse future prospects for productivity advances, including the role of science and technology, taking two contrasting industries (grains in Australia, dairy in New Zealand) to highlight the challenges and opportunities. In keeping with the pastoral and agronomic focus of this conference, the scope of the paper is limited to broadacre industries of the agriculture sector and hence excludes intensive industries such as vegetables, fruit, pig, poultry and eggs as well as the extensive rangelands systems found in inland Australia.
... The benefits of understanding the quantity of pasture available to livestock have been well documented (Fulkerson et al. 2005). By integrating a GPS unit with the Crop CircleÔ, it is possible to map the quantity and spatial variability of herbage mass across entire paddocks. ...
Article
Efficiently measuring and mapping green herbage mass using remote sensing devices offers substantial potential benefits for improved management of grazed pastures over space and time. Several techniques and instruments have been developed for estimating herbage mass, however, they face similar limitations in terms of their ability to distinguish green and senescent material and their use over large areas. In this study we explore the application of an active, near infrared and red reflectance sensor to quantify and map pasture herbage mass using a range of derived spectral indices. The Soil Adjusted Vegetation Index offered the best correlation with green dry matter (GDM), with a root mean square error of prediction of 288kg/ha. The calibrated sensor was integrated with a Global Positioning System on a 4-wheel motor bike to map green herbage mass. An evaluation of representative, truncated transects indicated the potential to conduct rapid assessments of the GDM in a paddock, without the need for full paddock surveys.
... Different feed allocation decisions will be necessary when cows graze tall pastures compared with those that graze short pastures. This was tested by Fulkerson et al. (2005) in two experiments where the effect of accurately allocating feed on a daily basis to lactating Holstein-Friesian dairy cows was assessed. They concluded that accurately allocating supplementary feed to maintain a constant grazing pressure was beneficial irrespective of the level of pasture on offer. ...
Article
To investigate how grazing time, herbage dry matter intake (DMI) and intake rate (IR) are influenced by intensive grazing management, dairy cows strip-grazing subtropical grass pastures (Pennisetum clandestinum) at two compressed sward heights (10 and 13 cm) and at five grazing durations (1, 2, 4, 8 and 15 h) and replicated over 3 days were studied. The study was conducted in summer and the cows were observed every 20 min from 1600 to 0700 hours to calculate the time spent (min/h) grazing, ruminating and resting. Total time spent grazing was 45 min longer for cows grazing the 13-cm sward than for those grazing the 10-cm sward over the 15-h grazing period. The rate of increase in grazing time was 0.64 h/h grazing duration up to 4 h after introduction to fresh pasture. IR of cows grazing the 13-cm sward was significantly higher than those grazing the 10-cm sward (0.17 v. 0.12 kg DM/min spent grazing). The difference in IRs between sward height treatments resulted from the higher DMI in the 13-cm sward within the first 4 h of grazing compared with the 10-cm sward, although following the first 4-h grazing period IR was similar for both sward heights. Grazing time increased with sward height up to a maximum of 4 h after introduction to fresh pasture and had also maximised herbage DMI by this time. These results have important practical implications for dairy cow grazing management systems because they show that dairy managers could remove cows after 4 h with little compromise in production and will help in developing optimum supplementary feeding strategies when pasture availability limits DMI.
... The accurate allocation of pasture and supplements on a daily basis have been shown to be important for optimising pasture utilisation and feed use efficiency by dairy cows on a pasture-based system of farming (Fulkerson et al., 2003). The general adoption of feed allocation by dairy farmers has been low due to the need to: obtain an accurate estimate of pasture mass (PM); calculate pasture available; and evaluate feeding options in terms of nutrient characteristics and the cost of rations. ...
Article
Accurate daily feed allocation to dairy cows is important for optimising the response to supplements and pasture utilisation in a pasture-based system of dairy farming. A major factor in feed allocation is obtaining an accurate estimate of pasture mass (PM). Pasture mass can be estimated, either visually or with one of a number of pasture meters (rising plate meter or electronic pasture probe). The tedium of walking the farm and in the calculations involved in converting this data into a form on which meaningful management decisions can be based, discourages farmers from adopting this technology. A database program, FeedSmart, has been developed to calculate pasture mass available, and pasture accumulation rate from weekly pasture walks, while animal requirements are obtained from live weight, milk production and calving pattern. The implications of using various supplementary feeding options to fill any feed deficit, in terms of total ration nutrient characteristics (metabolisable energy, crude protein and acid detergent fibre) and cost, are then calculated. FeedSmart has been refined to its present form by on-going input from practising dairy farmers and extension specialists. This paper outlines feed allocation methodology and its incorporation into a decision support tool.
... Home-grown feed remains a significant proportion of the diet for Australia's dairy herd however the overall cow diet has increasingly been balanced by other feed supplements. For many years, efficient utilisation of pasture has been consistently shown as a key profit driver for Australian dairy farms (Beca 2008) yet across the industry there is considerable room for improvement in pasture utilisation (Fulkerson et al. 2005). In an attempt to seek higher pasture production and utilisation across the industry, scientists have looked to increase the use of data driven approaches in grazing management decision making. ...
Article
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Grazing management practice is a central component in Australian pasture-based dairy systems and is largely based on application of tacit rules. This approach often conflicts with the quantitative decision making promoted in the scientific community. The development of pasture measurement and software tools have historically had a minimal or short-term influence on grazing management practice across the industry. The potential role of objective data in dairy farm management was examined using a five month, 18-dairy farm, trial of satellite-derived pasture data as a case study. The findings showed that when assessing sources of pasture data farmers looked at the accuracy and timeliness of data, in addition to the fit of data within existing information networks and its impact on uncertainty in planning. A tension existed between the grazing management approaches farmers preferred (simple, cost-effective, and fitting with their routines and goals) and the scientific worldview (objectivity and structured decision making) of those developing new means of gathering pasture data. To avoid continued underutilisation, future development of pasture measurement tools needs to provide greater consideration to the processes that dairy farmers use in practice.
... Na Figura 4, encontra-se a simulação da lotação animal, do ganho de carcaça por hectare e da receita bruta considerando-se a variabilidade espacial da produção de matéria seca no ano. Os benefícios da quantificação da disponibilidade de forragem para a pecuária foi bem documentada por Fulkerson et al. (2005). A Figura 4a indica que em cerca de 70% da área a oferta de forragem foi em média 23,5 t ha -1 de matéria seca, e em 17% da área foram produzidos torno de 25 t ha -1 de MS, sendo que no restante a produção foi entre 19,8 e 22,7 t ha -1 . ...
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The knowledge of spatial variability soil properties is useful in the rational use of inputs, as in the variable rate application of lime and fertilizers. PA requires methods to indicate the spatial variability of soil and crops for reducing the need for expensive and intensive sampling. The objective of this work was to map and evaluate spatial variability of soil electrical conductivity, biophysics parameters, yield and economical return of an intensively manages pasture. The study was conducted in an area of pasture Mombaça grass irrigated and intensively managed in a rotational system in Sao Carlos, SP, Brazil. Soil electrical conductivity (EC) was measured with a prototype of a contact sensor. NDVI and chlorophyll content (Chl) readings were taken during the summer season with a Crop Circle active optical sensor. Results showed that the NDVI and ECa had the same tendency of dry matter estimation. Results showed that EC map had the same tendency of dry matter production. Vegetation indexes have the potential to map the spatial variability of pasture production. PA tools were useful to establish the pasture spatial variability and support the management strategies. Resumo: O conhecimento da variabilidade espacial das propriedades do solo é útil para o uso racional dos insumos, como na aplicação a taxa variável de calcário e fertilizante. A AP necessita de métodos que indiquem a variabilidade espacial do solo e das culturas para reduzir a necessidade de amostragens intensivas e caras. O objetivo deste trabalho foi o mapear a variabilidade espacial da condutividade elétrica do solo, parâmetros biofísicos, produtividade e análise econômica de uma pastagem de capim-mombaça irrigada e manejada no sistema intensivo rotacionado em São Carlos (SP). A condutividade elétrica aparente do solo (CEa) foi medida com um protótipo de sensor de contato. O índice de diferença de vegetação normalizado (NDVI) e o teor de clorofila (Chl) foram medidos com o sensor óptico ativo Crop Circle. Os resultados mostraram que o mapa da CE apresentou com a mesma tendência da produção de matéria seca. Os índices de vegetação têm potencial para o mapeamento da variabilidade espacial da produção de forragem. As ferramentas de AP foram úteis para estabelecer a variabilidade espacial da pastagem e fornecer informações para as estratégias de manejo.
... The use of objective pasture data for decisionmaking in pasture-based farming has remained low (Parker, 1999;Gray, 2001) even after the development of measurement tools and with research highlighting economic benefits of enhanced pasture utilisation in dairy (Fulkerson et al., 2005). Research by Parker (1999) suggested that farmers preferred subjective monitoring methods due to associated speed and convenience and he also identified that farmers who adopted formal pasture measurement tools or techniques often moved to more intuitive and subjective methods once they had developed confidence in these methods. ...
... As an on-farm method, clipped sampling is not practical due to the time and labor required. However, indirect methods of measuring forage mass appear to be cost effective for improving management efficiency compared to management when forage mass is not known (4,10). ...
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A variety of tools are used for measuring pasture height or capacitance. Cross calibrations between these tools would be helpful for extension staff and producers comparing measurements taken with one tool to those taken with an alternative tool. Rotationally and continuously stocked pastures in West Virginia, Pennsylvania, Maryland, and New York were sampled for forage height using a ruler, for compressed height using a falling plate meter and a rising plate meter, and for sward capacitance with a capacitance meter. Thirty to sixty measurements were made across each pasture with each device, with paddock means taken as the measurement for the device. Regressions were run using paired paddock means, testing each device as both the dependent and independent variable, with r² ranging from 0.49 to 0.99. Residual analysis was conducted to evaluate biases due to location and stocking management using the falling plate meter means as the independent variable versus means of the other techniques. No bias in pasture measurements was found due to grazing management. There was a bias due to operator for ruler height and capacitance meter reading. These cross calibrations provide a mechanism for pasture managers to translate pasture heights or capacitance taken with one tool to those taken with another tool.
... Measuring pasture mass is difficult and time consuming, and therefore most farmers do not do it systematically (García and Fulkerson, 2005). Regular and systematic measurement of pasture could potentially not only increase average pasture utilization, but also milk production and profitability by feeding supplements in relation to pasture availability (Earle and McGowan, 1979;Fulkerson et al., 2005;French et al., 2015). ...
Article
An increase in the average herd size on Australian dairy farms has also increased the labor and animal management pressure on farmers, thus potentially encouraging the adoption of precision technologies for enhanced management control. A survey was undertaken in 2015 in Australia to identify the relationship between herd size, current precision technology adoption, and perception of the future of precision technologies. Additionally, differences between farmers and service providers in relation to perception of future precision technology adoption were also investigated. Responses from 199 dairy farmers, and 102 service providers, were collected between May and August 2015 via an anonymous Internet-based questionnaire. Of the 199 dairy farmer responses, 10.4% corresponded to farms that had fewer than 150 cows, 37.7% had 151 to 300 cows, 35.5% had 301 to 500 cows; 6.0% had 501 to 700 cows, and 10.4% had more than 701 cows. The results showed that farmers with more than 500 cows adopted between 2 and 5 times more specific precision technologies, such as automatic cup removers, automatic milk plant wash systems, electronic cow identification systems and herd management software, when compared with smaller farms. Only minor differences were detected in perception of the future of precision technologies between either herd size or farmers and service providers. In particular, service providers expected a higher adoption of automatic milking and walk over weighing systems than farmers. Currently, the adoption of precision technology has mostly been of the type that reduces labor needs; however, respondents indicated that by 2025 adoption of data capturing technology for monitoring farm system parameters would be increased.
... Na Figura 4, encontra-se a simulação da lotação animal, do ganho de carcaça por hectare e da receita bruta considerando-se a variabilidade espacial da produção de matéria seca no ano. Os benefícios da quantificação da disponibilidade de forragem para a pecuária foi bem documentada por Fulkerson et al. (2005). A Figura 4a indica que em cerca de 70% da área a oferta de forragem foi em média 23,5 t ha -1 de matéria seca, e em 17% da área foram produzidos torno de 25 t ha -1 de MS, sendo que no restante a produção foi entre 19,8 e 22,7 t ha -1 . ...
Chapter
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Entre os fatores que podem interferir negativamente no rendimento de grãos da soja, destacam-se os insetos-praga. O estudo da distribuição espacial de insetos-praga é fundamental para a utilização de estratégias de controle, aperfeiçoamento de técnicas de amostragens, quantificação de danos econômicos e incorporação de um programa de agricultura de precisão (AP) voltado para manejo integrado de pragas. Avaliar a distribuição espacial de pragas na cultura da soja por meio de ferramentas de AP e a possibilidade de controle sítio específico foi o objetivo desse estudo. O estudo foi realizado na safra agrícola 2007/2008 em uma área de 99,75 ha, localizada no município de Boa Vista das Missões, RS, Brasil. A área foi amostrada em malha de 100 m x 100 m (1 ha) totalizando 98 pontos amostrais. Para fins de comparação, 40,27 ha foram manejados segundo princípios da AP e 59,48 ha segundo a metodologia convencional. Durante a safra investigada realizaram-se 13 avaliações de pragas semanalmente. Este estudo indicou que a utilização das ferramentas de AP em associação as do Manejo Integrado de Pragas - MIP mostram-se promissoras na redução do custo de produção e no incremento da sustentabilidade da produção de soja.
... A proactive approach to allocate pasture forage to animals must consider grazing management as a set of dynamic decisions that take into account the temporal and spatial variation of pasture growth associated mainly to weather, soil nutrients and grazing management factors. However, this approach can be time consuming and requires adequate methods and techniques to systematically monitor changes in pasture cover [1]. ...
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Systematic monitoring of pasture quantity and quality is important to match the herd forage demand (pasture removal by grazing or harvest) to the supply of forage with adequate nutritive value. The aim of this research was to monitor, assess and manage changes in pasture growth, morphology and digestibility by integrating information from an Unmanned Aerial Vehicle (UAV) and two process-based models. The first model, Systems Approach to Land Use Sustainability (SALUS), is a process-based crop growth model used to predict pasture regrowth based on soil, climate, and management data. The second model, Morphogenetic and Digestibility of Pasture (MDP), uses paddock-scale values of herbage mass as input to predict leaf morphogenesis and forage nutritive value. Two field experiments were carried out on tall fescue- and ryegrass-based pastures under rotational grazing with lactating dairy cattle. The first experiment was conducted at plot scale and was used to calibrate the UAV and to test models. The second experiment was conducted at field scale and was used to test the UAV’s ability to predict pasture biomass under grazing rotation. The Normalized Difference Vegetation Index (NDVI) calculated from the UAV’s multispectral reflectance (n = 72) was strongly correlated (p < 0.001) to plot measurements of pasture biomass (R² = 0.80) within the range of ~226 and 4208 kg DM ha⁻¹. Moreover, there was no difference (root mean square error, RMSE < 500 kg DM ha⁻¹) between biomass estimations by the UAV (1971±350 kg ha⁻¹) and two conventional methods used as control, the C-Dax proximal sensor (2073±636 kg ha⁻¹) and ruler (2017±530 kg ha⁻¹). The UAV approach was capable of mapping at high resolution (6 cm) the spatial variability of pasture (16 ha). The integrated UAV-modeling approach properly predicted spatial and temporal changes in pasture biomass (RMSE = 509 kg DM ha⁻¹, CCC = 0.94), leaf length (RMSE = 6.2 cm, CCC = 0.62), leaf stage (RMSE = 0.7 leaves, CCC = 0.65), neutral detergent fiber (RMSE = 3%, CCC = 0.71), digestibility of neutral detergent fiber (RMSE = 8%, CCC = 0.92) and digestibility of dry matter (RMSE = 5%, CCC = 0.93) with reasonable precision and accuracy. These findings therefore suggest potential for the present UAV-modeling approach for use as decision support tool to allocate animals based on spatially and temporally explicit predictions of pasture biomass and nutritive value.
... Efficient and profitable milk production from pasture centres around the utilisation of grazed grass (Shalloo et al., 2011) as this is the cheapest home produced feedstuff on dairy farms (Finneran et al., 2010). Consistent allocation of sufficient pasture on a daily basis can result in ~ 10 % higher milk yield (Fulkerson et al., 2005). Pasture allocation is dependent on a number of factors such as the assessment of the quantity of biomass, animal requirements which can be influenced for example by stage of lactation and the quality of the pasture, but the primary determinant is available biomass. ...
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Grazing is the natural feed intake behaviour of a cow. However, in the last century, intensive confinement systems with silage feeding and concentrate supplementation have replaced many extensive pasture-based milk production systems. Grazed grass is now acknowledged as the cheapest feed available as a consequence of rising machinery, labour and feeding costs. Thus there is a renewed interest in intensive pasture-based milking systems. In addition, policy objectives, societal expectations and environmental concerns have all supported reconsiderations for pasture-based milk production. Novel technology to aid measuring and managing grassland and cow grazing behaviour have the potential to facilitate improved performance. Until recently, sensor technologies for dairy farms were mainly developed for measuring feeding behaviour of housed cows. Adapting and calibrating these technologies to grazing context would therefore further support improved pasture-based dairying. In this thesis, two sensor technologies were validated against visual observation. The RumiWatch noseband sensor (Itin+Hoch, Switzerland) is a high precision technology designed for research applications. It can measure detailed grazing behaviour such as grazing bites, rumination chews, time spent grazing and time spent ruminating. The MooMonitor+ (Dairymaster, Ireland) is the second technology assessed in this thesis. It is a collar based accelerometer and is primarily designed for use on commercial farms. The initial development was for oestrus detection. It can now monitor grazing and rumination times. The results of the studies reported in this thesis revealed that both sensors were highly accurate compared to visual observation. The implementation of sensor technology on commercial dairy farms is still slow. This is especially true on pasture-based dairy systems. The management of grazing cows is thus largely not supported by technology. With increasing herd sizes and skilled labour shortages, sensor technology to support grazing management will likely improve some major dairy farm management challenges. A key factor in pasture-based milk production is the correct grass allocation to maximize the grass utilization per cow. Cow behaviour is indicative of the quantity and quality of feed available as well as animal performance, health and welfare. Thus, the measurement of cow grazing behaviour is an important management indicator. A further study of detailed individual grazing behaviour aimed to identify behavioural indicators of restricted versus sufficient availability of grass. Such objective measurement has potential since currently grass allocation is based on subjective eye measurements and calculations per herd. To identify behavioural indicators, a group of 30 cows in total were allocated a restricted pasture allowance of 60 % of their intake capacity. Their behavioural characteristics were compared to those of 10 cows with pasture allowance of 100 % of their intake capacity. The grazing behaviour and activity of cows was measured using the RumiWatchSystem, consisting of the noseband sensor and pedometer. The results showed that bite frequency was continuously higher for cows with a restricted grass allocation, but also rumination behaviour was affected by the restriction. This study contributes vital information towards developing a decision support tool for automated allocation of grass based on feedback from individual cows rather than herd based measurements. Further research activities should focus on identification of significant changes in grazing behaviour of cows at individual animal and herd level. This would allow implementation of specific thresholds to be used in decision support tools. After developing and validating the decision support tools, the application of automated solutions for grazing management can improve efficiency and productivity of pasture-based milk production systems.
... Grazed pasture is the basis of profitable dairy systems in temperate regions such as New Zealand, Australia and Ireland (Neal et al. 2019). Optimal use of this resource requires regular measurement of pasture availability (usually expressed in kgDM/ha), as well as estimation of future growth rates (Fulkerson et al. 2005;Hanrahan et al. 2017;Beukes et al. 2018). Current methods of onfarm pasture measurement include: visual assessment; calibrated rising-plate meter measurement (RPM); and vehicle-mounted sensors (L'Huillier & Thomson 1988;Dalley et al. 2009;Eastwood et al. 2009). ...
Article
Regular estimation of pasture availability is a time-consuming on-farm task, but one that is vital for good grazing management. The ability to automate this task is, therefore, highly valuable. Combining satellite sensing of pasture mass with global positioning for herd location provides raw data that can potentially be used to automatically estimate pasture mass, pasture growth and pasture grazing events across a farm. The feasibility of automatically obtaining and processing this information was demonstrated on a Waikato dairy farm from 22 October 2018 to 21 February 2019 (123 days), with 13 global positioning collars recording the location of grazing mobs 16 times per hour on average, in a dairy herd of initially 380 animals. Satellite sensing of pasture cover over the same period was only possible on 16 days during this period, with November being particularly cloudy, resulting in fewer pasture cover estimates. A non-linear regression model was constructed with parameters representing initial pasture cover, average pasture growth rate through time, pasture growth differences between paddocks, pasture disappearance rate relative to the density of cow GPS samples, and an ungrazeable residual. A Bayesian approach was used to infer the model parameters from the satellite-measured pasture cover data. This allowed interpolation of pasture mass through the whole period with an RMSE of 225 kgDM/ha, as well as identifying growth rate differences between paddocks, which may provide a useful basis for improved pasture management. Rough estimates of cow average daily pasture disappearance were also made, which peaked at 20 kgDM/d in November, falling to 5 kgDM/d by February. This pilot study demonstrated the feasibility of combining satellite pasture cover data with herd location data from a small number of GPS collars to infer pasture growth rates in individual paddocks through time.
... In rotational grazing systems, a variation of 10% of milk yield can be explained by the fresh grass available to animals (Fulkerson et al., 2005). The allocation of fresh grass regulated by the rotation of the cows in the pastures is therefore critically important to keep high milk yield. ...
Article
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Highlights • Main behaviours of dairy cows were successfully predicted using accelerometer data. • EXtreme Gradient Boosting followed by the Viterbi algorithm led to the best results. • Postures are the most difficult to discriminate with an accelerometer on the neck. • 86 Holstein cows from 4 farms were equipped and observed leading to a large dataset. • Independent signal sequences with a stratification were used to validate the models.
... Grazed pasture is generally the most cost-effective nutrient source in pasture-based dairy systems (Dillon et al., 2008), making it imperative to maximize annual pasture consumption (t of DM/ha) without unduly compromising individual cow performance (Peyraud and Delagarde, 2013). Achieving this requires rotational grazing systems that accurately allocate pasture to minimize wastage (over-allocation) or compromise pasture and cow performance (under-allocation;Fulkerson and Donaghy, 2001;Fulkerson et al., 2005;Roche et al., 2017). Virtual fencing is promoted as the next advancement for rotational grazing systems. ...
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Virtual fencing is promoted as the next advancement for rotational grazing systems. This experiment compared the capacity of conventional temporary electric versus virtual fencing to contain a herd of 30 lactating dairy cows within the boundaries of their daily pasture allocation (inclusion zone). Cows were moved each day to a new rectangular paddock that was divided crosswise into an inclusion and exclusion zone by a single linear electric (first 10 d) or virtual (second 10 d) front-fence. A 3-d virtual fence training period separated the 2 treatments. Virtual fences were imposed using a pre-commercial prototype of the eShepherd virtual fencing system (Agersens Pty Ltd.). Neckband-mounted devices replaced the visual cue of an electric fence with benign audio cues, which if ignored were accompanied by an aversive electrical stimulus. Cows learned to respond to the audio cues to avoid receiving electrical stimuli, with the daily ratio of electrical to audio signals for individual cows averaging (± standard deviation) 0.18 ± 0.27 over the 10 d of virtual fence deployment. Unlike the electric fence, the virtual fence did not fully eliminate cow entry into the exclusion zone, but individual cows were generally contained within the inclusion zone ≥99% of the time. Pasture depletion within the inclusion zone reduced the efficacy of the virtual fence in preventing cows from entering the exclusion zone, but the magnitude of this effect was insignificant in practical terms (i.e., increased time spent in the exclusion zone by ≤28 s/h per cow). This highlights the potential for virtual fences to control grazing dairy cow movement even when pasture availability is limited (i.e., 1 kg of dry matter/cow above a target residual of 1,500 kg of dry matter/ha), but requires confirmation under longer and more complex virtual fencing applications. Within each treatment period, uniform daily pasture utilization (% of pasture consumed above a target residual of 1,500 kg of dry matter/ha) within inclusion zones indicates that cows did not avoid grazing near electric or virtual front-fences. Overall, this study demonstrated a successful simple application of this virtual fencing system to contain a herd of grazing lactating dairy cows within the boundaries of their daily pasture allocation.
... If the herbage allowance varies from low to high, the benefits estimated here may be reduced. The impact of variable herbage allocation on milk production and profit has been previously demonstrated, with a more consistent herbage allocation shown to increase milk yield by 9% for cows grazing ryegrass pasture [30]. In a modelling study that investigated different levels of knowledge about pasture mass, Beukes et al. [31] found that annual farm operating profit could be increased by 11-15% if pasture mass could be estimated with an error of 15% of less. ...
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The economics of grazing dairy cows offered a range of herbage allowances and fed supplements as a partial mixed ration (PMR) were examined where profit was defined as the margin between total milk income and the cost of pasture plus PMR supplement. The analysis made use of milk production and feed intake data from two dairy cow nutrition experiments, one in early lactation and the other in late lactation. In early lactation and at a PMR intake of 6 kg DM/cow per day, the profit from the cows with access to a medium herbage allowance (25 kg DM/cow per day) was AUD 1.40/cow per day higher than that for cows on a low allowance (15 kg DM/cow per day). At a higher PMR intake of 14 kg DM/cow per day, the profit from the cows on a medium herbage allowance was AUD 0.45/cow per day higher than the cows on a low allowance; there was no additional profit from increasing the herbage allowance from medium to high (40 kg DM/cow per day). In late lactation, the profit from the cows fed a PMR with a medium herbage allowance (20 kg DM/cow per day) was only higher than the cows on a low allowance (12 kg DM/cow per day) when the PMR intake was between 6 and 12 kg DM/cow per day. There was also a difference of AUD +0.50/cow per day between the PMR with medium and high herbage allowance (32 kg DM/cow per day). It was concluded that farmers who feed a PMR to dairy cows should offer at least a medium herbage allowance to optimize profit. While feeding additional PMR increases milk production and profit, further gains would be available by offering a higher herbage allowance. These findings provide an estimate of the net benefits of different herbage allowances when feeding a PMR and will enable farmers to manage their feeding systems more profitably.
Article
This paper focuses on dairy herd performance in the United Kingdom and southern Australia, where feed costs have been estimated to comprise between 40 and 67% of the total costs of production. The efficiency of conversion of grazed pasture, home grown forages and purchased feeds into milk has a major bearing on farm profit. Feed conversion efficiency (FCE), defined as 'kg milk of standardised composition with respect to protein and fat concentrations produced per kg feed dry matter consumed', is a useful measure of the performance of a farm feeding system, but is seldom used by dairy farmers. It could also be defined as 'g protein plus fat produced per kg feed dry matter consumed', given that farmers are often paid for these components. The value of estimating FCE on an annual or shorter-term basis is discussed in relation to accepted principles of feed utilisation and dairy cow energy requirements. The implications of feed intake, conversion of ingested nutrients into absorbed nutrients and the subsequent utilisation of these nutrients for milk production or other purposes, as well as the effects of stage of lactation on FCE, are reviewed. Measuring FCE and identifying opportunities for improvement is relatively straightforward in housed feeding systems, but is more problematic under grazing. Hence, approaches and the key assumptions in estimating FCE in grazing situations, as well as possible limitations of these estimates, are discussed. Finally, a case study examining the potential impact of improved nutritional strategies on FCE and on margin over feed costs is presented. It is concluded that, to remain profitable, dairy farmers need to have a sound knowledge of cow nutrition, along with appropriate measures of FCE to monitor the performance of their milk production system. Such indicators of the biological performance of the farming system are most useful when used in conjunction with appropriate measures of economic performance.
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The adaptation success of inexperienced heifers to a pasture-based automated milking system (AMS) is likely to influence the viability and feasibility of the system. This study evaluated two heifer training programs designed to improve adaptation success to determine their impact on early lactation performance within a pasture-based AMS. The two training programs involved heifers calving in spring 2007 (S07) and autumn 2008 (A08) being managed as members of the milking herd for 2 weeks, starting 2 months before their expected calving date. In each calving season, heifers were randomised into two treatment groups with one being fed and teat sprayed (FTS) while visiting the milking stations, while the other group passed through (PT) the milking stations. Milk yield, milking frequency and waiting time were selected as indicators of early lactation performance within an AMS and were reported as values relative to those measured by their experienced herdmates (within calving season). Longer relative waiting times (hours spent in pre-milking area where experienced herdmates = 1.0; FTS: 1.36 vs PT: 1.95; P = 0.006) may have been one cause of the low relative milk production levels reported (range 0.49 S07PT-0.78 A08PT). The milking frequencies of the heifers were in line with their experienced herdmates (range 0.83 S07PT-1.10 A08PT). There were no significant differences between the two training programs, indicating that farmers could adopt either program and expect similar early lactation heifer performance. The choice of program would more likely be affected by AMS utilisation levels, costs of consumables (teat spray and concentrate feed) and any existing desire to feed concentrate to heifers during the training period.
Article
In this study, the effect of increasing the proportion of concentrate in the diet, on efficiency of feed utilisation, was determined when Holstein–Friesian cows grazed short-rotation ryegrass (Lolium multiflorum) or kikuyu (Pennisetum clandestinum) pastures. The concentrates were energy-dense dairy pellets fed twice-a-day at milking and the roughage component was lucerne hay and the pasture.When cows grazed ryegrass, there was no effect on animal performance as the proportion of concentrate in the diet increased from 0.23 to 0.35 (4.75 to 7.50kg concentrate/cow/day). The substitution rate of concentrates for pasture for the first 1.57kg concentrate/cow/day fed was 0.58 but rose to 1.18 for the next 1.28kg concentrate/cow/day.When cows grazed kikuyu, there was also no effect of increasing the proportion of concentrate in the diet on total dry matter intake (DMI) or milk production. However, there was a substantial increase in the in vivo digestibility of whole diet, pasture and acid detergent fibre (ADF) was observed when the proportion of concentrate in the diet increased from 0.08 to 0.25. However, there was a marked decline in pasture digestibility (72% to 64%), and more so in ADF digestibility (61.3% to 48.4%), as the proportion of concentrate in the diet increased further to 0.29 (5.52kg/cow/day). The intake of kikuyu, when determined by difference between pre- and post-grazing pasture mass, was substantially underestimated compared to the use of the n-alkane technique, and this discrepancy increased as the pasture on offer increased.On both pasture types, the neutral detergent fibre intake, as a % of bodyweight varying from 1.6% to 2.2% for kikuyu and 1.5% to 1.6% for ryegrass was far above the values claimed of 1.2% to restrict intake.The results of this study highlight the limits to the amount of concentrate that can be fed on a typical Australian dairy farm where concentrates can only be fed twice-a-day at milking. The results also provide a more appropriate benchmark for fibre limitation in the ration when cows graze pasture, particularly poorer quality tropical grasses, and this value is well above that found in more intense feeding situations.
Article
Milk production per cow and per farm in the irrigated region in northern Victoria have increased dramatically over the past 2 decades. However, these increases have involved large increases in inputs, and average productivity gains on farms have been modest. Before the early 1980s, cows were fed predominantly pasture and conserved fodder. There is now large diversity in feeding systems and feed costs comprise 40-65% of total costs on irrigated dairy farms. This diversity in feeding systems has increased the need to understand the nutrient requirements of dairy cows and the unique aspects of nutrient intake and digestion in cows at grazing. Principles of nutrient intake and supply to the grazing dairy cow from the past 15 years' research in northern Victoria are summarised and gaps in knowledge for making future productivity gains are identified. Moreover, since the majority of the milk produced in south-eastern Australia is used in the manufacture of products for export, dairy companies have increased their interest in value-added dairy products that better meet nutritional requirements or provide health benefits for humans. Finally, some examples of the impacts of farm system changes on operating profit for some case study farms in northern Victoria are presented to illustrate the need for thorough analysis of such management decisions.
Article
Ten paired irrigated dairy farms under biodynamic ( BD) and conventional ( CV) management were compared over a 3-year period (1991-93). The paired farms were located in the irrigation districts of northern Victoria and southern New South Wales and were matched for soil type, cattle breed and farm area. The BD farms practised BD principles for an average of 16 years before the study. The effects of farm management on milk yields and composition and animal health were examined by annually surveying farm managers regarding disease incidence and chemical treatment of animals, and by measuring milk yield and composition and faecal egg counts over the experimental period. The two hypotheses tested were that (1) milk volume and milk solids per cow would be lower under BD management, and (2) the incidence of internal parasitic infection and disease would be lower under CV management. Milk production and milk components produced, both on a per hectare and per cow basis, were 24-36% higher (P < 0.01) under CV management, due to significantly higher pasture intakes (P < 0.001). Although the incidence of parasitic infection was similar for mature cows, CV farms consistently used a greater number of chemical treatments (P < 0.05). Although BD heifer calves < 8 months in age had significantly (P < 0.05) higher faecal egg counts, the results highlight the risk of reduced growth rates in calves due to high rates of parasite infection, under both management systems. Somatic cell counts were higher (P < 0.05) under BD management, with this being consistent with the use of significantly less chemical treatments under this management system. The implications of these findings for both CV and BD management for milk production and animal health are discussed.
Thesis
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Les prairies, en déclin depuis les années 1970, peuvent jouer un rôle majeur dans la transition agroécologique compte tenu de leurs nombreux atouts. Leur valorisation par le pâturage est néanmoins complexe à mettre en œuvre et conditionne directement les bénéfices attendus. Des outils numériques se développent pour optimiser la gestion du pâturage dans le cadre de l’élevage de précision, un concept qui s’appuie sur la révolution numérique. Ces outils restent cependant marginaux et leur plus-value est généralement mal perçue par les éleveurs. Le comportement et la position des vaches laitières devraient servir de support au développement de ces outils car ils sont des indicateurs potentiels de l’état de la ressource sur la parcelle, de la santé et du bien-être des animaux. Des capteurs accéléromètres et GPS embarqués permettent de remonter automatiquement ces informations à condition de mettre en œuvre des techniques d’analyses adaptées. Ce travail de thèse consiste (i) à mettre en place une méthodologie permettant de remonter automatiquement les principaux comportements des vaches laitières au pâturage à partir de capteurs accéléromètres et (ii) d’évaluer le potentiel de cette méthodologie combinée à des données de position pour répondre aux applications envisagées. Le cadre méthodologique développé s’appuie sur des techniques de traitement du signal non explorées dans la communauté concernée, associées à une combinaison d’algorithmes qui met en jeu la complémentarité entre des méthodes de machine learning et des modèles probabilistes. Il garantit ainsi une prédiction fiable pour un large spectre de comportements des vaches laitières au pâturage. La preuve de concept réalisée témoigne également du potentiel de la méthodologie, combinée à des données de position des animaux, pour détecter des troubles de confort en lien avec les conditions de pâturage. Cette approche pourrait donc servir de support au développement d’outils d’aide à la décision pour l’optimisation de la gestion du pâturage, constituant ainsi un levier potentiel dans la transition agroécologique.
Article
During the last decade, Australian dairy farmers have been challenged to increase total factor productivity ( the ratio between the rate of increase in total output and the rate of increase in the use of all inputs) in order to attenuate the negative effects of a steady decline in the terms of trade over the same period of time. Overall, the increase in total factor productivity has been low (1.5%) and farmers are questioning the most appropriate production system for the future. In an attempt to address this central question, we first identified the nature of the key pressures dairy farmers in Australia are likely to face in the future, namely labour and feed related issues. We then discuss major opportunities for developing new dairy production systems based on increased efficiency in the use of land and cows and on increasing the efficiency of labour management and lifestyle. We do not attempt to provide the best futuristic option for dairy systems in Australia. Instead, this review discusses key areas of the production system with potential to impact positively on any or all the physical, economic and labour-related aspects of modern dairy farming. By so doing, this review highlights the research questions that need to be addressed now in order to provide Australian dairy farmers with improved tools to manage their production systems in the future.
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Pasture management is one of the most important factors in developing a successful dairy production system in New Zealand. Key performance indicators include the quantity of pasture grown and how well it is utilized. For a pasture management system to be successful, it requires accurate pasture measurement and the ability to use the collected information to make decisions at all management levels. Industry commentators often talk about a ten per cent improvement in production being possible from improved utilization of pasture. On a national scale a 10% improvement would result in an increased return of NZ$560M. (US$400M) to dairy farmers. Improvement in pasture production and utilization has been hampered by the lack of tools to accurately measure pasture production in a way that is time efficient and cost effective. This needs to be managed so that demand can be met and excess conserved. This paper describes work which provides an ATV mounted yield mapping capability for dairy farmers. The most common form of pasture measurement previously used has been the rising plate meter (RPM). During the testing phase it was found there was a strong linear relationship between the two measurement instruments (R 2 = 0.955) in terms of predicting pasture dry matter (kg DM ha -1), when exactly the same points were used. When full paddock measurements were undertaken by taking measurements at a ten metre swath width using the new high speed sensor there was found to be a significant difference between the predicted dry matter readings and those gained by a single transect of the plate meter. Indicating that the transect was not necessarily a representative sample.
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Pre-commercial virtual fence (VF) neckbands (eShepherd®, Agersens, Melbourne, Vic, Australia) can contain cows within a designated area without the need for physical fencing, through associative learning of a paired audio tone and electrical pulse. Cattle are gregarious, so there may be an impact of herd mates on the learning process. To evaluate this, a VF was set 30 m down one of three test paddocks with a feed attractant 70 m past the VF. Twenty-three Holstein-Friesian cows were all fitted with VF neckbands and trained as individuals or in groups (5–6) for four 10 min tests; then, cows were crossed over to the alternate context for two more 10 min tests. The number of cows breaking through the VF and the number of paired stimuli reduced across time (from 82% to 26% and 45% to 14%, respectively, p < 0.01). Cows trained in a group (88%) were more likely to interact with the VF in the crossover compared to those trained as individuals (36%) (p < 0.01), indicating an influence of group members on individual cow response. Individual training is impractical, therefore, future research should evaluate group training protocols ensuring all cows learn the VF to avoid any adverse impacts on animal welfare.
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________________________________________________________________________________ Abstract This trial was conducted to test the hypothesis that early lactation cows grazing ryegrass pasture and receiving maize and mineral supplementation could respond to additional supplementation with a protein source such as fish meal. Multiparous Jersey cows in early to mid lactation that grazed annual ryegrass pasture in spring were used in a randomised complete block design experiment. In addition to the pasture, cows received 6 kg (as is) of a maize-based supplement, including minerals, fed in two equal portions in the milking parlour. Three groups of 15 cows received a control, a low fish meal or a high fish meal treatment (0, 4 or 8% fish meal replacing maize). Milk yield was measured and milk samples taken fortnightly. A simultaneous study on rumen fermentation was conducted using eight rumen cannulated cows receiving the control and high fish meal treatments in a cross-over design experiment. Ruminal pH and ammonia-N and volatile fatty acid concentrations were measured. Milk yield, 4% fat-corrected milk yield and milk fat and protein percentages of cows on the low and high fish meal treatments (21.9 and 22.1 kg milk/d, 24.1 and 24.2 kg 4% fat corrected milk/d, 4.73 and 4.67% fat and 3.49 and 3.45% protein) were higher than the control (20.5 kg milk/d, 20.4 kg 4% fat corrected milk/d, 3.97% fat and 3.25% protein). The ruminal ammonia-N concentration was higher in the cows on the high fish meal treatment than the control (16.7 vs. 14.2 mg/dL). Fish meal supplementation to cows on ryegrass proved to be profitable.
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Precision livestock farming uses biosensors to measure different parameters of individual animals to support farmers in the decision making process. Although sensor development is advanced, there is still little implementation of sensor-based solutions on commercial farms. Especially on pasture-based dairy systems, the grazing management of cows is largely not supported by technology. A key factor in pasture-based milk production is the correct grass allocation to maximize the grass utilization per cow, while optimizing cow performance. Currently, grass allocation is mostly based on subjective eye measurements or calculations per herd. The aim of this study was to identify possible indicators of insufficient or sufficient grass allocation in the cow grazing behaviour measures. A total number of 30 cows were allocated a restricted pasture allowance of 60% of their intake capacity. Their behavioural characteristics were compared to those of 10 cows (control group) with pasture allowance of 100% of their intake capacity. Grazing behaviour and activity of cows were measured using the RumiWatchSystem for a complete experimental period of 10 weeks. The results demonstrated that the parameter of bite frequency was significantly different between the restricted and the control groups. There were also consistent differences observed between the groups for rumination time per day, rumination chews per bolus and frequency of cows standing or lying.
Article
New technologies that can allow measurement and exploitation of biological variation to improve resource efficiency are rapidly becoming available. Some of these technologies can be applied to improve the efficiency of pasture-based systems. There will be significant innovation in technology for capturing variation in dairy-cow productivity and welfare, as the potential market globally is very large; however, the market potential for technology for pasture-based grazing systems is much smaller and will require public funding to stimulate innovation in technology, to capture and exploit the variation in pasture production and utilisation. Current research in Teagasc Moorepark is focussed on developing and adapting technology to capture both the inter-paddock and intra-paddock variation in pasture production that will potentially allow more specific and efficient nutrient use and higher total herbage production. The second focus of the current research is in the development of technologies to capture and manage the variation in grass utilisation by real-time monitoring and collating the data on herd output and post-grazing residual and controlling individual-animal pasture allocation through individual GPS-location identification and control with virtual fencing.
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In animals consuming known amounts of supplement, total intake of roughage plus supplement could be estimated by dividing known supplement intake by the supplement proportion in the diet, if the latter were estimated using the alkane patterns in roughage, supplement and the faeces of the animals consuming them. The roughage intake could then be obtained by multiplying total intake by the estimated roughage proportion in the diet. This approach was tested using data from a previous study, and the results of a second experiment. In the first experiment, perennial ryegrass chaff and sunflower meal labelled with beeswax alkanes were fed in different proportions to young sheep. Known roughage intake was significantly under-estimated (P<0.05) by about 8% using this approach, due mainly to the 12% under-estimate in animals receiving the lowest level of supplement. This reflected the compounding of errors in estimating the dietary proportion of supplement in that treatment. In the other treatments, there was a smaller and non-significant difference between known and estimated roughage intakes. In the second experiment, similar animals were fed either subterranean clover chaff alone or known proportions of chaff and cottonseed meal labelled with beeswax. There was excellent agreement between the known proportions of supplement in the diet and those estimated using alkane patterns in the chaff, meal and the faeces obtained by total collection. As a result, total intakes and thus chaff intakes were estimated very accurately; mean estimated chaff intake was less than 1% different from known mean chaff intake. The results suggest that in animals such as the dairy cow, where daily supplement intake is known or can be controlled, the intake of pasture or its components could be estimated using the approach described, without the need to dose the animals with synthetic alkanes. INTRODUCTION The n-alkanes of plant cuticular wax can be used to estimate the diet composition and herbage intake of grazing animals (Mayes et al. 1986; Dove and Mayes 1996). Estimates of herbage intake are derived from the herbage and faecal concentrations of long-chain alkanes adjacent in chain length (e.g., C32, C33 alkanes) and the daily dose rate of the even-chain alkane. The odd-chain alkane is derived from herbage, and the method requires that animals be dosed with the even-chain alkane, administered either daily (or more frequently) as gelatin capsules or paper pellets, or administered only once as an intra-ruminal, controlled-release device (Dove and Mayes 1996). Diet composition can be estimated by relating the alkane pattern in the faeces, adjusted for incomplete alkane recovery, to the alkane patterns of the various species available for consumption by the animal, using least-squares mathematical procedures (e.g., Dove and Moore 1995; Newman et al. 1995). If the animals are also consuming supplements, then the proportion of supplement in the diet, and ultimately supplement intake, can be estimated by treating the supplement as one of the 'species' in the diet. Dove and Oliván (1998) extended this concept to supplements that contain no alkanes (e.g., solvent-extracted oilseed meals), by labelling them with a source of alkanes such as beeswax.
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This review examines the use of changes in soluble carbohydrate reserves, and the onset of senescence in ryegrass (Lolium spp.), as key criteria for successfully managing an intermittent grazing system for dairy cattle. Ryegrass is a ‘3-leaf ’ plant; that is, only about 3 green leaves/tiller exist at any one time with the initiation of a new leaf coinciding with senescence of the oldest fourth leaf. Thus, grazing pasture older than 3 leaves/tiller will not only lead to wastage of pasture but also the senescent material will reduce overall quality of herbage. Based on this, the time taken for 3 new leaves/tiller to regrow sets the maximum grazing interval. On the other hand, in a well-utilised dairy pasture, most ryegrass leaf has been removed and the plant relies on stored water-soluble carbohydrate reserves to grow new shoots and hence regain photosynthetic capacity. If the concentration of water-soluble carbohydrates is inadequate, because there has been insufficient time to replenish in the previous inter-grazing period, regrowth will be suppressed and this may also affect persistence in the longer term. Immediately after grazing, water-soluble carbohydrate reserves decline as they are used to regrow new shoots, and root growth stops. It is not until about 3/4 of a new leaf/tiller has regrown that the plant has adequate photosynthetic capacity for growth and maintenance and only then does water-soluble carbohydrate replenishment and root growth commence. Studies have shown that subsequent regrowth is suppressed if plants are redefoliated before the 2 leaves/tiller stage of regrowth. Also, the levels of potassium and nitrogen (as nitrates and other non-protein nitrogen products) may be very high and cause metabolic problems in stock grazing such pasture. Thus, replenishment of water-soluble carbohydrate reserves sets the minimum grazing interval at 2 leaves/tiller. The rate of accumulation of water-soluble carbohydrates in the plant is a function of input through photosynthesis (source) and output to growth and respiration (sinks). Thus, apart from grazing interval (which sets the time to replenish water-soluble carbohydrate plant reserves), water-soluble carbohydrate storage will be influenced by incoming solar radiation (cloud cover, day length, pasture canopy density) and energy needs of the plant through respiration (temperature, canopy mass) and growth. Relating grazing interval to leaf number places the emphasis on the readiness of plants to be grazed rather than on the animals’ requirements, with leaf appearance interval depending primarily on ambient temperature. This allows grazing interval to be expressed in a similar morphological stage of growth, irrespective of season or location. Setting grazing interval on these 2 criteria has been shown to maximise growth and persistence of ryegrass and optimise the levels of most nutrients in pasture required by dairy cattle including protein, water-soluble carbohydrates, calcium, potassium and magnesium. Metabolisable energy and fibre do not change appreciably up to the 3 leaves/tiller stage of regrowth. On the other hand, grazing pasture before 2 leaves/tiller not only retards regrowth and reduces persistence, it provides forage too high in potassium and protein (nitrates) and too low in water-soluble carbohydrates for dairy cattle.
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The feasibility of estimating, with acceptable precision, temperate and tropical pasture mass in a subtropical environment was evaluated. For the tropical grasses kikuyu (Pennisetum clandestinum) and setaria (Setaria ancepts), 3 different pasture meters were calibrated against 1 of 3 dry matter (DM) estimates. Temperate pastures (predominantly Lolium perenne and Trifolium repens) were evaluated using the Ellinbank rising plate meter (RPMl) calibrated against DM to ground level. A single regression equation was developed for syegrass-white clover pasture from data pooled within season over first- and second-year swards. The s.e. of estimate (s.e.e.) for assessing tropical grass pasture mass using RPMl was similar to that for a heavier Ellinbank meter and substantially lower than that for the electronic pasture probe. Using RPMl, separate regression equations were required for early (November-February) and late (March-May) season determinations for both kikuyu and setaria. The regression equations were based on calibrations against shoot DM (>5 cm stubble height for kikuyu and 6 cm for setaria) and are only applicable to well-managed and highly utilised pastures. Calibrations of all pasture meters over all months to green DM (senescent leaf and stem removed) gave a lower s.e.e. than total DM (kikuyu 138 v. 177 kg DM/ha, n = 171; setaria 211 v. 224 kg DM/ha, n = 177) whilst shoot DM gave a more substantial reduction in s.e.e. (kikuyu 95 v. 147 kg DM/ha; setaria 140 v. 193 kg DM/ha).
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Two studies were conducted to compare the precision of estimating kikuyu grass (Pennisetum clandestinum) intake by Friesian cows fed 0, 3 or 6 kg of cereal-based concentrate/cow.day, using a rising plate meter (RPM), standard energy requirements in reverse (RS) and plant wax alkanes as internal markers. Study 1 compared herbage intake estimates obtained using the RPM and RS techniques over a 45-day period. RS estimates were based on the metabolisable energy (ME) of ration components derived from in vitro organic matter digestibility (OMD) values. Pregrazing calibration equations for the RPM determined at 2-weekly intervals differed significantly (P<0.01) from postgrazing calibrations; consequently separate equations were used to determine pasture intake as the difference in pre- and post-grazing pasture mass. Estimates of total intake were lower using the RPM than the RS technique for the groups fed 0 kg (12.5 v. 14.8 kg dry matter (DM)/cow.day) and 3 kg (10.4 v. 12.9 kg DM/cow.day) of concentrate, and higher for those receiving 6 kg (10.5 v. 7.8 kg DM/cow.day). In study 2 (12 days duration), intakes derived using alkanes were compared with intakes estimated using the RS and RPM techniques. The C32/C33 alkane pair gave the closest estimate of herbage intake to that obtained using the RPM and RS techniques. Whole diet in vivo DM digestibility (DMD), determined by the alkane method, was not significantly different between the 3 groups (mean 70%), suggesting that digestibility of the kikuyu declined with increasing concentrate supplementation. The in vivo DMD of kikuyu alone (determined in the non-concentrate-supplemented cows) was considerably higher (69.5%) than the OMD determined in vitro (63.9%). By using in vivo rather than in vitro digestibilities for kikuyu in the RS calculations, the intake estimates were reduced by 17%, and for the 0 kg concentrate group, intake estimates aligned closely to predictions of the RPM and alkanes. Concentrates in the diet resulted in lower intake estimates using the RS technique compared with the RPM and alkane techniques. This was most evident at the 6 kg level of supplementation where RS predicted kikuyu intake to be 6.5 kg DM/cow.day using in vivo-derived DMD and this was substantially lower than either the RPM (12.4 kg DM/cow.day) or alkanes (9.2 kg DM/cow.day). The alkane technique provided a direct and precise method of measuring the intake of individual cows grazing tightly-managed kikuyu pasture. With the use of accurate animal production and feed quality parameters, the RS technique can provide sensible pasture intake estimates over an extended time period. The RPM technique is useful for obtaining herd estimates of pasture intake and for the determination of pasture parameters associated with intake.
The capacity of cetyl trimethylammonium bromide to dissolve proteins in acid solution has been utilized in development of a method, called acid-detergent fiber method (ADF), which is not only a fiber determination in itself but also the major preparatory step in the determination of lignin. The entire procedure for determining fiber and lignin is considerably more rapid than presently published methods. Compositional studies show ADF to consist chiefly of lignin and polysaccharides. Correlations with the new fiber method and digestibility of 18 forages (r = —0.79) showed it to be somewhat superior to crude fiber (r = —0.73) in estimating nutritive value. The correlation of the new lignin method and digestibility was —0.90 when grass and legume species were separated.
Article
Accurate daily feed allocation to dairy cows is important for optimising the response to supplements and pasture utilisation in a pasture-based system of dairy farming. A major factor in feed allocation is obtaining an accurate estimate of pasture mass (PM). Pasture mass can be estimated, either visually or with one of a number of pasture meters (rising plate meter or electronic pasture probe). The tedium of walking the farm and in the calculations involved in converting this data into a form on which meaningful management decisions can be based, discourages farmers from adopting this technology. A database program, FeedSmart, has been developed to calculate pasture mass available, and pasture accumulation rate from weekly pasture walks, while animal requirements are obtained from live weight, milk production and calving pattern. The implications of using various supplementary feeding options to fill any feed deficit, in terms of total ration nutrient characteristics (metabolisable energy, crude protein and acid detergent fibre) and cost, are then calculated. FeedSmart has been refined to its present form by on-going input from practising dairy farmers and extension specialists. This paper outlines feed allocation methodology and its incorporation into a decision support tool.
Article
An automatic rising plate meter called the Ellinbank Pasture Meter (EPM) was constructed and evaluated at the Dairy Research Institute (Ellinbank) for measuring the dry matter present on a pasture dominated by green perennial rye grass. The meter readings of pasture height were found to correlate linearly with pasture yield, and the coefficient of variation of calibrations on any one date averaged about 13%. When the data from a large number of calibrations from separate dates were pooled, the coefficient of variation increased to about 18% (RSD 370 kg ha-1). Although there was a high level of repeatability of readings within operators, a substantial degree of variation existed between operators. The diurnal variation in dry matter percentage of pasture had only small effects on the meter readings. The EPM has an advantage over other pasture measuring methods in that its automatic function enables up to one hundred pasture measurements to be made in five minutes. In direct comparison with a manual rising plate meter and a two probe electronic capacitance meter, it was found that there was no significant difference in accuracy between the three meters but the advantages of the EPM are discussed. On perennial rye grass/white clover pastures in an actively growing vegetative state, the EPM has been found to be accurate enough for research purposes yet simple enough for use by farmers and their advisers as an aid to pasture management.
Article
This review discusses the potential use of plant wax components, especially n-alkanes, as markers for estimating herbage intake, estimating the botanical composition of consumed herbage and studying digesta kinetics. Previous approaches to making these measurements are discussed briefly. Attention is drawn to the fact that current methods for estimating intake do not adequately allow for differences between individual animals. It is also suggested that the markers currently used to estimate botanical composition or study digesta kinetics are inadequate. The nature of the chemical constituents of plant waxes is briefly discussed and the concept of using alkanes to estimate intake is introduced. Particular emphasis is given to the fact that although the recovery of alkanes in faeces is not complete, intake can still be estimated using a pair of alkanes (one natural, one dosed) provided these have similar faecal recoveries. The accuracy of estimation of intake is discussed in terms of: obtaining a representative sample of herbage; alkane dosing and faecal sampling procedures; validity of the assumption of similar recoveries for the natural and dosed alkanes; sample preparation and analysis. Published comparisons of estimated and actual intakes are presented, with the conclusion that satisfactory results are obtained if intake is estimated using natural C33 alkane and dosed C32 alkane. The use of the different patterns of alkanes in herbage species, as a means of estimating botanical composition, is then discussed. Results are presented showing this can be done successfully with herbage mixtures or oesophageal extrusa. Procedures are then described for making the corrections for incomplete faecal alkane recovery, necessary to estimate the botanical composition of the herbage consumed by the free-grazing animal. This allows the quantification of the intake of individual plant species by individual animals, and it is suggested that this can be achieved without the need for oesophageally-fistulated (OF) animals. Differences in alkane levels between plant parts within a species are then discussed. It is suggested that these can lead to error in the estimation of intake, if OF animals should consume plant parts different from those consumed by the test animals. However, it is also suggested that differences in alkane levels between plant parts can be used to quantify the intake of these parts, in a manner analogous to the estimation of the intake of individual plant species. The usefulness of alkanes in studies of digesta kinetics is then discussed, principally in relation to the natural alkanes, which remain intimately associated with plant particles in the gut. It is suggested that natural alkanes could prove excellent markers for studies of particle breakdown and digesta flow. The preparation of natural 14C-labelled alkane, for use as a pulse dose in mean retention time studies, is also discussed.
Feeding standards for Australian livestock
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An improved electronic capacitance meter for estimating pasture yields
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The productivity of Friesian cows: effect of genetic merit and level of concentrate feeding. Final report to the Dairy Research and Development Corporation
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Fulkerson WJ, Hough G, Goddard M, Davison T (2001) The productivity of Friesian cows: effect of genetic merit and level of concentrate feeding. Final report to the Dairy Research and Development Corporation.