Search

The nutrition of grazing ruminants can be optimized by allocating pasture according to its nutritive characteristics, provided that nutritive concentrations are determined in near-real time. Current proximal spectrometers can provide accurate predictive results but are bulky and expensive. This study compared an industry standard, 'control', proximal spectrometer, often used for scientific estimation of pasture nutrient concentrations in situ (350–2500 nm spectral range), with three lower-cost, 'next-generation', handheld spectrometers. The candidate sensors included a hyperspectral camera (397–1004 nm), and two handheld spectrometers (908–1676 nm and 1345–2555 nm respectively). Pasture samples (n = 145) collected from two paddocks on a working Australian dairy farm, over three timepoints, were scanned in situ by each instrument and then analysed for eight nutritive parameters. Chemometric models were then developed for each nutrient using data from each sensor (split into 80:20 calibration and validation sets). According to Lin's Concordance Correlation Coefficient (LCCC) from independent validation (n = 29), the hyperspectral camera was the best candidate instrument (LCCC from 0.31 to 0.85, and 0.67 on average), rivalling the control sensor (LCCC from 0.41 to 0.84, and 0.67 on average). Consideration was given to whether the hyperspectral camera's success was due to spectral range or data type/capture method. It was found that the 400–920 nm (trimmed) spectral region was slightly less sensitive in principle to nutrient concentrations than higher spectral ranges. Therefore, the predictive performance of the camera was attributed to the advantage of gathering data as hyperspectral images as opposed to single spectra. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of Australia and New Zealand. However, it can have poor persistence due to a low tolerance to both abiotic and biotic stresses. A major challenge in measuring persistence in pasture breeding is that the assessment of pasture survival depends on ranking populations based on manual ground cover estimation. Ground cover measurements may include senescent and living tissues and can be measured as percentages or fractional units. The amount of senescent pasture present in a sward may indicate changes in plant growth, development, and resistance to abiotic and biotic stresses. The existing tools to estimate perennial ryegrass ground cover are not sensitive enough to discriminate senescent ryegrass from soil. This study aimed to develop a more precise sensor-based phenomic method to discriminate senescent pasture from soil. Ground-based RGB images, airborne multispectral images, ground-based hyperspectral data, and ground truth samples were taken from 54 perennial ryegrass plots three years after sowing. Software packages and machine learning scripts were used to develop a pipeline for high-throughput data extraction from sensor-based platforms. Estimates from the high-throughput pipeline were positively correlated with the ground truth data (p < 0.05). Based on the findings of this study, we conclude that the RGB-based high-throughput approach offers a precision tool to assess perennial ryegrass persistence in pasture breeding programs. Improvements in the spatial resolution of hyperspectral and multispectral techniques would then be used for persistence estimation in mixed swards and other monocultures. [ABSTRACT FROM AUTHOR]

This study aimed to develop empirical pasture dry matter (DM) yield prediction models using an unmanned aerial vehicle (UAV)-borne sensor at four flying altitudes. Three empirical models were developed using features generated from the multispectral sensor: Structure from Motion only (SfM), vegetation indices only (VI), and in combination (SfM+VI) within a machine learning modelling framework. Four flying altitudes were tested (25 m, 50 m, 75 m and 100 m) and based on independent model validation, combining features from SfM+VI outperformed the other models at all heights. However, the importance of SfM-based features changed with altitude, with limited importance at 25 m but at all higher altitudes SfM-based features were included in the top 10 features in a variable importance plot. Based on the independent validation results, data generated at 25 m flying altitude reported the best model performances with model accuracy of 328 kg DM/ha. In contrast, at 100 m flying altitude, the model reported an accuracy of 402 kg DM/ha which demonstrates the potential of scaling up this technology at farm scale. The spatial-temporal maps provide valuable information on pasture DM yield and DM accumulation of herbage mass over the time, supporting on-farm management decisions. [ABSTRACT FROM AUTHOR]

Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for livestock producers to use their agricultural lands more productively and intensively within sustainable limits. Breeding perennial ryegrass cultivars that are both productive and persistent is a target of forage breeding programs and will allow farmers to select appropriate cultivars to deliver the highest profitability over the lifetime of a sward. Conventional methods for the estimation of pasture persistence depend on manual ground cover estimation or counting the number of surviving plants or tillers in a given area. Those methods are subjective, time-consuming and/or labour intensive. This study aimed to develop a phenomic method to evaluate the persistence of perennial ryegrass cultivars in field plots. Data acquisition was conducted three years after sowing to estimate the persistence of perennial ryegrass using high-resolution aerial-based multispectral and ground-based red, green and blue(RGB) sensors, and subsequent image analysis. There was a strong positive relationship between manual ground cover and sensor-based ground cover estimates (p < 0.001). Although the manual plant count was positively correlated with sensor-based ground cover (p < 0.001) intra-plot plant size variation influenced the strength of this relationship. We conclude that object-based ground cover estimation is most suitable for use in large-scale breeding programs due to its higher accuracy, efficiency and repeatability. With further development, this technique could be used to assess temporal changes of perennial ryegrass persistence in experimental studies and on a farm scale. [ABSTRACT FROM AUTHOR]

• The value of extra pasture DM for livestock production can be estimated using several different methods. • The two-price market value method can be an efficient way to estimate the value of extra seasonal dry matter yield of pasture grown and used. • To demonstrate its application, the method is used to value the seasonal yield of 19 perennial ryegrass cultivars grown in temperate Australia. • The value of extra pasture estimated using the two-price market value method can be easily generalised across a range of farming systems. The challenges of estimating the value of growing and using extra pasture to the farm business often necessitates using complex systems modelling of individual farms. In this paper, a straightforward market value method is outlined, where technical substitutes for which there is a defined market price are used as proxies to estimate an equivalent value for extra pasture used in a farm system. The method can be useful for all grazed livestock production systems where substitutes for pasture are purchased and sold. The two-price approach presented advances on previous applications of using market values by accounting for differences in the value of extra pasture DM used on farm in different seasons. The value per extra kg DM harvested in seasons when pasture typically limits livestock production, for example during summer/autumn in temperate environments, and used on farm equates to the market price of the replacement cost as purchased supplementary feed. This is the maximum the extra pasture DM could be worth when used in the farm system. Conversely, the value per extra kg DM harvested in spring when extra pasture is likely to be in surplus of livestock requirements, equates to its market price if sold, as standing hay for example, termed the salvage value. This two-price approach was applied to value the previously published seasonal DM yield differences of 19 perennial ryegrass cultivars, compared to an industry standard cultivar, grown in three regions of Victoria, Australia. Results were then compared to the value of extra pasture DM previously estimated using system simulation analyses of three farms and applied to the same perennial ryegrass data set. The difference in the value of extra pasture DM estimated using the two methods ranged from $0.01/kg DM to $0.24/kg DM. Overall, the two-price method estimated an extra $24 to $200/ha.year benefit over the industry standard cultivar for the 19 cultivars across the three regions, similar to the extra $0 to ∼ $180/ha.year estimated by the previous system simulation analysis. The two-price market value method can be an efficient way to estimate the value of extra seasonal DM yield of pasture, with results easily generalised across a range of farming systems. [ABSTRACT FROM AUTHOR]

For Australian and New Zealand dairy farms, the primary source of home-grown feed comes from grazed perennial pastures. The high utilisation of perennial pasture is a key factor in the low cost of production of Australian and New Zealand dairy systems and, hence, in their ability to maintain international competiveness. The major pasture species used are perennial ryegrass (Lolium perenne L.) and white clover (Trifolium repens L.), normally grown in a simple binary mixture. As pasture production has been further driven by increasing use of nitrogen fertiliser and irrigation, farms are getting closer to their economic optimum level of pasture utilisation. Increasing inputs and intensification have also increased scrutiny on the environmental footprint of dairy production. Increasing the diversity of pasture species within dairy swards presents opportunities to further increase pasture utilisation through additional forage production, extending the growing season, improving forage nutritive characteristics and, ultimately, increasing milk production per cow and/or per hectare. Diverse pastures also present an opportunity to mitigate some of the environmental consequences associated with intensive pasture-based dairy systems. A consistent finding of experiments investigating diverse pastures is that their benefits are due to the attributes of the additional species, rather than increasing the number of species per se. Therefore, the species that are best suited for inclusion into dairy pastures will be situation specific. Furthermore, the presence of additional species will generally require modification to the management of dairy pastures, particularly around nitrogen fertiliser and grazing, to ensure that the additional species remain productive and persistent. Diverse pastures offer benefits to pasture-based dairy systems in terms of increased forage and animal production, improved resilience and the potential to reduce nitrate leaching. The management required to maintain the diversity in the pastures may limit their application. However, the benefits of diverse pastures can be archived with only three species making their management and application simpler. [ABSTRACT FROM AUTHOR]

• Ten mathematical models were created to predict pasture yield and nutritive value • Model inputs were field spectrophotometer readings (with or without added covariates) • The models can be applied in situ without destructive sampling to analyse pasture • The pasture yield, crude protein, and energy fraction models predicted most accurately • Models should not be applied where there is substantial bare ground exposure To optimise grazing livestock nutrition, it is necessary to know both the available dry matter yield and the nutritive characteristics of pasture at the farm-scale in near real time. Previous studies have shown the potential of using field spectrophotometers that measure the reflectance of light across the visible to near infrared spectrums to gather information on pasture dry matter yield (DMY) and nutritive characteristics. This study sought to calibrate and validate new mathematical models for ten parameters including dry matter yield and nine nutritive characteristics of relevance to ruminant nutrition. As a part of the analysis process, two innovative approaches were tested: the use of a hybrid modelling approach where partial least squares regression (PLSR) outputs were used as support vector regression (SVR) inputs; and, the inclusion of covariate data. These approaches were compared with traditional stand-alone PLSR and SVR modelling approaches without covariates. The study was undertaken in six predominantly perennial ryegrass pastures on a single farm in the temperate zone of South-Eastern Australia. A total of 204 pasture samples were scanned with a field spectrophotometer over several spring growth stages in late 2019 and subsequently analysed by wet chemistry to obtain reference nutritive values. The raw reflectance spectra were initially pre-processed using a variety of techniques and then used to test the four kinds of chemometric models. In cross validation, hybrid models showed a superior fit for all variates in comparison to the other model types tested. However, the differential was reduced in independent validation where, out of 10 best-performing models for dry matter yield and key nutrient properties, six were produced by the hybrid modelling, three from SVR and one from PLSR. For every hybrid model that was built, adding covariate(s) consistently improved model performance but the increase was small (a reduction in normalised root mean square error (RMSE) of -0.36 % on average for all properties considered). The best performing models were comparable with other published literature with normalised RMSE of prediction ranging from 1.7 – 23.1 % (a mean of 9.7%). Well-predicted variates included metabolisable energy, digestible energy, DMY, and crude protein. Fibre fractions, ash and dry matter were less well-predicted but still had acceptable normalised RMSE values (< 10 %) while carbohydrate fractions were the poorest predicted variates. It was concluded that hybrid modelling in chemometric analyses can modestly improve accuracy and shows promise as an alternative to more traditional approaches. Using covariates also improved accuracy, but the additional time and effort to gather such information outweighed the minor benefits of inclusion. [ABSTRACT FROM AUTHOR]