Pasture Nutrient Research Articles

Comparative Analysis of Feature Importance Algorithms for Grassland Aboveground Biomass and Nutrient Prediction Using Hyperspectral Data

Estimating forage yield and nutrient composition using hyperspectral remote sensing is a major challenge. However, there is still a lack of comprehensive research on the optimal wavelength for the analysis of various nutrients in pasture. In this research, conducted in Hailar District, Hulunber City, Inner Mongolia Autonomous Region, China, 126 sets of hyperspectral data were collected, covering a spectral range of 350 to 1800 nanometers. The primary objective was to identify key spectral bands for estimating forage dry matter yield (DMY), nitrogen content (NC), neutral detergent fiber (NDF), and acid detergent fiber (ADF) using principal component analysis (PCA), random forests (RF), and SHapley Additive exPlanations (SHAP) analysis methods, and then the RF and Extra-Trees algorithm (ERT) model was used to predict aboveground biomass (AGB) and nutrient parameters using the optimized spectral bands and vegetation indices. Our approach effectively minimizes redundancy in hyperspectral data by selectively employing crucial spectral bands, thus improving the accuracy of forage nutrient estimation. PCA identified the most variable bands at 400 nm, 520–550 nm, 670–720 nm, and 930–950 nm, reflecting their general spectral significance rather than a link to specific forage nutrients. Further analysis using RF feature importance pinpointed influential bands, predominantly within 930–940 nm and 700–730 nm. SHAP analysis confirmed critical bands for DMY (965 nm, 712 nm, and 1652 nm), NC (1390 nm and 713 nm), ADF (1390 nm and 715–725 nm), and NDF (400 nm, 983 nm, 1350 nm, and 1800 nm). The fitting accuracy for ADF estimated using RF was lower (R2 = 0.58), while the fitting accuracy for other indicators was higher (R2 ≥ 0.59). The performance and prediction accuracy of ERT (R2 = 0.63) were noticeably superior to those of RF. In conclusion, our method effectively identifies influential bands, optimizing forage yield and quality estimation.

Sistemas de información geográficos y vehículos aéreos no tripulados para la evaluación de suelos y pastos

The objective of this review is to present soil and pasture evaluation studies using georeferenced aerial photographs captured by sensors on board drones and analyzed using the Geographic Information System (GIS) to develop strategies for use in the management of pastures and farm potential. The use of intensive grazing systems requires advanced knowledge for efficient management, and smart and precision agriculture represents a strategy to reduce costs. Using GIS and drones, an immediate comprehensive diagnosis is obtained, such as quantification of the degradation of a pasture or farm, distribution of botanical composition, and variability of soil and pasture nutrients to generate fertilization plans by specific area (precision agriculture).

Strip grazing: Changes in biomass, nutrient content and digestibility of temperate, midsummer pasture by strip-grazed or ‘free’-grazing ponies, over 4 weeks

The digestibility and nutritive values of pastures that were either freely or strip-grazed for 28 days were compared. Twelve ponies were individually grazed in adjacent, 10 m wide, rectangular paddocks. On day -1, each paddock length was adjusted to contain a 28-day supply of herbage DM at 1.5 % of pony BW/d. Ponies, (4/treatment) either accessed the entire 28-day supply from day 1 (TA), or gradually via strip-grazing treatments, SG1 and SG2. SG1 had a ‘lead’ fence spanning the paddock width that from day 3 was moved forward 1/26th of the paddock length daily, whereas SG2 had an additional ‘back fence’, that was advanced the same distance as the lead fence daily. Pasture nutrient contents and estimated nutrient digestibilities (eD) were determined weekly. From weeks 1-4, mean strip-grazed pasture contents (% DM) of water-soluble carbohydrate (WSC) and crude protein declined from 15.7-10.2 and 9.4-8.7, respectively, whereas neutral- and acid-detergent fibre increased from 53.6-60.5 and 35.2-38.0, respectively. Corresponding values for TA pastures were 17.7-5.2, 8.9-8.7, 54.6-69.2 and 35.7-43.6. Concomitantly, calculated digestible energy (cDE) (MJ/kg DM) of strip-grazed and TA pastures declined from 9.7-8.9 and 9.6-7.8, respectively and eDMD of all treatments declined (P<0.05). By week 4, TA pasture WSC, cDE and eDMD were lower and fibre contents higher (P<0.05) than the strip-grazed pastures. TA animal cDE intakes in weeks 1 and 2 exceeded requirements by 42 % vs. 8 % by strip-grazed animals. Strip-grazing delivered a steadier nutrient supply than TA, potentially conferring health benefits to animals prone to metabolic dysfunction.

Comparing the predictive ability of Sentinel-2 multispectral imagery and a proximal hyperspectral sensor for the estimation of pasture nutritive characteristics in an intensive rotational grazing system

Using remote sensing to estimate the nutritive characteristics of pasture in livestock systems is a rapidly developing area that has the potential to improve pasture management and utilisation and in turn improve livestock productivity. The Copernicus Sentinel-2 paired satellites have shown promise in predicting pasture nutritive characteristics and have a pixel size small enough to be able to identify intra-paddock nutrient variation in rotational dairy grazing systems where paddock sizes are small. This study sought to develop nutrient prediction models using a dataset of nine Sentinel-2A and -2B satellite images, by linking imagery to perennial ryegrass dairy pasture samples cut within known pixels of the images in the days directly after each cloud-free overpass. Just before destructive pasture sampling, a handheld, proximal, hyperspectral sensor was used to gather spectral signatures (400–2500 nm) from areas to be sampled. Pasture samples (n = 200) were analysed using wet chemistry techniques for seven nutritive characteristics. The dry matter concentration of the samples was also measured, creating eight variates to be modelled in total. The Sentinel-2 data from within the selected pixels was extracted and used to create input features for Random Forest prediction models. Input features included the raw multispectral bands, common vegetation indices, and independent variables ‘season’ and ‘month’. Predictive models were also developed from the hyperspectral sensor data using Partial Least Squares Regression. For both modelling types, variable selection was used to reduce model inputs to only those sensitive to each nutrient. Results comparing the final models in an independent validation test showed similar predictive performance of the Sentinel-2 satellite and the proximal hyperspectral sensor for all variates of interest, shown by mean absolute errors that were not significantly different. In each case, the dry matter concentration was the best predicted variable (Lin’s concordance correlation coefficient (LCCC) > 0.90) with excellent potential to be quantified by Sentinel-2 satellites. Crude protein, metabolisable energy, neutral detergent fibre, non-fibre carbohydrate, and water-soluble carbohydrate concentrations were also predicted with good to moderate accuracy by both sensors (LCCC between 0.50 and 0.80). These models would be sufficiently accurate for making qualitative predictions (e.g., grouping into high to low categories). Ash and acid detergent fibre concentrations were poorly predicted (LCCC < 0.50) and these models would not be recommended for further use. It was concluded that multispectral data from Sentinel-2 has great potential to estimate pasture nutrient concentrations for intensively grazed pastures such as those used in dairy systems in temperate regions.

118 Nutrient Cycling Potential from Manure from Heifers Fed Four Bermudagrass Cultivars

Abstract Maintenance of pastures for grazing accounts for $132.50/animal in the United States (23% of operating costs), and pasture nutrient input represents the single greatest influence in productivity per unit area. Cultivar differences in the forage offered to cattle have the potential to manifest differences in digestive efficiency, resulting in different nutrient excretion profiles. The objective of this study was to assess the growth potential and incorporation of N, P, and K into forage material from manure from cattle fed one of four bermudagrass (Cynodon dactylon [L.] Pers.) cultivars. Ruminally fistulated heifers (n = 4) were assigned randomly to one of four bermudagrass cultivars (Coastal, Russell, Tifton 44, or Tifton 85) for four 30-d in vivo periods (21-d adaptation and 9-d collection). On d 23, total fecal collections were subsampled from each heifer. As a hierarchical addition, a controlled greenhouse experiment was conducted for each period [15 pots per period; 5 manure treatments (four bermudagrass diets plus a negative control) × 3 replications]. Manure was added to 4.58 L pots based on stocking rate estimates for Central Alabama (1,110 kg/ha). The pots were seeded with bermudagrass (cv. Giant) as an indicator for forage nutrient uptake. At harvest (d 70), herbage, root, and soil samples were collected. Herbage and root samples were assayed for CP. Soil samples were assayed for pH, N, P, K, Mg, and Ca. Data were analyzed using SAS v. 9.4. There was no effect of manure treatment on herbage (P = 0.88) or root DM yield (P =.00;.54 and.73 g/pot, respectively). Similarly, there was no effect of manure treatment on soil pH (P = 0.18; 5.5), P (P = 0.78; 24.2 kg/ha), or K (P = 0.42;56.7 kg/ha). Results are interpreted to mean that, regardless of the cultivar effects on ruminal digestive dynamics, there is little to no effect of the resulting manure on subsequent forage production and nutrient uptake.

75 Development of a rapid screening assay for monitoring the relative nutritional value of pasture samples

Pasture with a high biodiversity has the potential to provide a balanced diet and year-round forage availability for grazing equids. Monitoring the influence of biotic and abiotic factors on the relative proportions of key nutrients in pasture could help to inform grazing strategies. Traditional wet chemistry forage analytical methods are labor-intensive and require large sample amounts, thereby limiting their usefulness for high sample throughput. To develop a high throughput screening assay, 3 different hydrolysis methods were compared, each allowing the simultaneous extraction of total hydrolyzable carbohydrates (THC), protein, and phenolics for subsequent spectrophotometric quantification using established microassays. Three pasture samples were collected from mixed species grassland plots (3m x 2m, SRUC Aberdeen, UK). These samples represented a diverse range of THC, protein, and phenolic contents and included high- and low-sugar perennial ryegrass and forage herbs plots (collected in June 2021) and a herbal ley plot (collected in August 2021). Freeze-dried and milled samples were extracted in triplicate and subjected to a range of hydrolysis conditions (Method 1: 2.4% H2SO4, 2 h at 100°C; Method 2: 3M HCl, 14 h at 100°C; Method 3: 2M HCl, 0.5 h at 95°C) before spectrophotometric quantification (Lever assay for reducing sugars,Ninhydrin assay for proteins, Fast Blue assay for phenolics). Solvent-matched calibration curves of Bovine serum albumin (BSA), gallic acid, and glucose/fructose standards were used to quantify analytes and evaluate optimal hydrolysis conditions for each analyte to maximize yield. Extractions were repeated across 3 consecutive days to assess reproducibility of each hydrolysis method. All 3 hydrolysis methods demonstrated a reproducibility and reliability > 99% for THC and phenolics across all samples. ANOVA (using SPSS: significance level set at P < 0.05) showed significant differences between the means of phenolics and THC concentrations for method 2 vs. method 1 (P = < 0.001) and method 2 vs. method 3 (P = 0.015). Method 3 was identified as most optimal for the quantification of THC and phenolics, resulting in analyte concentrations reflective of the expected seasonal and grass species differences in the samples. However, the short hydrolysis time of this method was not sufficient for optimal protein quantification as achieved by Method 2. Therefore, a combination of Method 2 and 3 was subsequently established and is undergoing further optimization to evaluate performance in a greater range of pasture samples.

Growing season grazing promotes the shallow stratification of soil nutrients while non-growing season grazing sequesters the deep soil nutrients in a typical alpine meadow

Grazing management and climate are known to be important drivers influencing soil nutrient accumulation. The change of soil nutrients in pasture depends on the excreta return and the vegetation regeneration. Livestock activities significantly affect plant growth and soil nutrients through feeding, trampling, and excretion. At the same time, grazing time and climate change are stimulating plant growth and altering plant composition. How long-term specific grazing management will affect soil nutrient storage and movement under a warming-humid climate trend needs further study. In 20-year grazing management trial, conducted in a typical alpine meadow in the Qilian Mountains on the Qinghai-Tibet Plateau, sampled and analyzed the soil from under a non-growing season graze pasture (WP) and a growing season graze pasture (SAP) in August 1999 and August 2019 along grazing gradients (GG) at different distances from the pasture entrance (0, 300, 600, 900, 1200, and 1500 m). The experimental results of 2019 compared to 1999 show that SAP and WP increased soil available nitrogen density (SAND) and availability (SAND:soil total nitrogen density [STND] ratio) in the first 300 m along the grazing gradient soil, while SAP showed a decrease in soil available phosphorus density (SAPD) and availability (SAPD:soil total phosphorus density [STPD] ratio), and WP had no significant difference between gradients. Grazing in spring and autumn increased soil organic carbon density (SOCD), and STND in the first 300 m along the gradient soil, whereas it had the reverse effect in WP. At the same time, STPD has no obvious changing trend along the gradient. Growing and non-growing season grazing increased the SOCD:STND ratio in the first 300 m along the grazing gradient but resulted to a lower STND:STPD ratio. The comparison found that long-term grazing from 1999 to 2019 increased the SAND and SAPD. SAP improved soil total C, N, and P density in the topsoil (0–10 cm), but WP improved it throughout the profile (0–40 cm). These findings indicate that time of grazing and the grazing gradient affect the accumulation and migration of different elements under long-term grazing. Furthermore, SAP and WP had a positive effect on the accumulation of topsoil and deep soil nutrients, respectively. We suggest that periodic pasture exchange in SAP and WP should be carried out to maintain the sustainability of the ecosystem. At the same time, for different gradients, especially the first 300 m, regular soil nutrient monitoring and human intervention (such as fertilization) should be carried out to eliminate potential element restrictions and focus on the development of possible problems caused by the livestock locked in the pens at night, such as nutrient enrichment (the excreta as the organic fertilizer and soil improvement material for farmland) and loss (element footprint) in pens, which are of important guiding significance for the grazing system’s stable development of alpine meadow.

Comparing how accurately four different proximal spectrometers can estimate pasture nutritive characteristics: effects of spectral range and data type

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.

Effects of supplementation on production and age anticipation at first mating of ewe lambs in tropical pastures.

Studies on the rearing of ewe lambs in the Cerrado are scarce, so the objective was to evaluate the effects of protein-energy supplementation with 1.6 and 2.4% BW on the productive and reproductive performance of ewe lambs raised on pastures of Brachiaria brizantha cv. Marandu. Twenty-four Texel ewe lambs, with a mean age of 5months, were distributed in two treatments with provision of energy protein supplementation at levels of 1.6 and 2.4% BW, formulated for average daily gains of 150 and 200g/day for ewe lambs weighing 20kg, respectively. The experimental delineation adopted was the completely randomized design, with fifteen repetitions per treatment. The performance of the ewe lambs was evaluated by weight at the beginning and end of the breeding season (BS), average daily gain (ADG), and total weight gain (TWG). The count of fecal eggs was performed monthly. The evaluation of the ewe lambs reproductive organs took place at 8months of age. The fertility of the ewe lambs was detected by transrectal ultrasonography at the end of the BS. The supplementation level of 2.4% BW was higher for ADG and TWG; however, it did not influence the other performance characteristics. There was no effect of supplementation on the reproductive characteristics, parasite load, and nutrients of the pasture. Pasture nutrients were influenced by the month of use. Supplementation levels were not effective to ensure the reproductive success of 8-month-old ewe lambs.

Assessing the impacts of biochar and fertilizer management strategies on N and P balances in subtropical pastures

Improper nutrient management strategies often results in soil N and P imbalances, which can lead to unintended environmental consequences. Nutrient budgets can be useful tools to identify nutrient imports and exports and assess the agronomic and environmental performance of different pasture nutrient management options. In this 3-yr study, we used a farm-gate nutrient budgeting approach to determine N and P use efficiency in subtropical perennial pasture systems subjected to different nutrient management strategies. Treatments consisted of repeated application of biosolids and inorganic fertilizer (at an equivalent rate of 160 kg plant available N ha−1 yr−1) either alone or in combination with (20 Mg ha−1 yr−1). Major N and P inputs and outputs were: N and P added via fertilizer and biochar, plant uptake, leaching, and denitrification (N budget only). Nitrogen recovery in forage above-ground biomass accounted for a significant proportion of applied N (~64%) and P (22%). Greater N leaching occurred in inorganic fertilizer (29% of applied N) vs. biosolids treatments (average of 7% of applied N). Negligible P leaching (0.1–0.2% of total P applied) occurred, regardless of the treatment. Biochar did not affect either crop N and P use efficiency or P leaching, but reduced N leaching for treatments receiving inorganic fertilizer. Treatments showed no impact on soil N, but vertical distribution of soil total P changed over 3 yr, suggesting downward movement of P. Despite the high initial soil test P levels, biosolids application at P loads exceeding agronomic recommendations improved agronomic N and P efficiency relative to control treatments with no impact on N or P leaching.

110 Fecal pH, dry matter and volatile fatty acids of horses grazing legume-grass mixed pastures

Abstract Inclusion of legumes into grass pastures improves pasture quality while significantly decreasing the use of nitrogen (N) fertilizer. Changes in nutritive value of the pasture can affect gut microbiome and health. This study aimed to determine the effects of bahiagrass (Paspalum notatum) pasture intercropped with rhizoma peanut (RP, Arachis glabrata) on forage nutrient composition and fecal variables. Unfertilized bahiagrass (UNF) with no N fertilizer, bahiagrass with 120 kg N.ha-1 (FER), and bahiagrass intercropped with RP and 30 kg N.ha-1 (RP) pastures were evaluated using twelve mature Quarter Horses continuously stocked for 84 days in a randomized block design. Forage and fecal samples were collected every 28 days. Pasture samples were analyzed for nutrient composition by NIR, and fecal samples were analyzed for dry matter (DM), fecal fluid pH and volatile fatty acids (VFA). Data were analyzed using a repeated measure mixed model ANOVA and associations between variables were tested using Pearson’s correlations. Pasture starch was higher (P &amp;lt; 0.01) and non-fiber carbohydrates tended to be higher (P &amp;lt; 0.1) in RP. Hemicellulose and aNDF tended (P &amp;lt; 0.1) to be higher in UNF than in RP. Horses grazing RP tended (P &amp;lt; 0.1) to have greater fecal fluid propionic acid concentration and lower proportion of butyric acid. Proportion of acetic acid was lower (P &amp;lt; 0.05) in FER. Propionic acid concentration was negatively correlated with aNDF (r = -0.55) and ADF (r = -0.52), and positively correlated with ethanol-soluble carbohydrates (ESC, r = 0.55). Butyric acid proportion was negatively correlated with nonstructural carbohydrates (r = -0.70). Horses grazing FER pasture tended to have lower (P &amp;lt; 0.1) fecal DM when compared to RP. Fecal DM was negatively correlated with hemicellulose (r = -0.51), and positively correlated with water-soluble carbohydrates (r = 0.68) and ESC (r = 0.64). Intercropping RP provides pasture nutrient composition superior to UNF and similar to FER with decreased N fertilizer rate, and stimulates positive fermentation outcomes.

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

Milk production of crossbred holstein × zebu on different pasture-management strategies

The objective of this study was to evaluate nutrient intake and digestibility, yield and components of milk from Holstein × Zebu cows on Tanzania grass pastures using a fixed-rest period or 95% light interception. The treatments consisted of evaluations of two Tanzania-grass pasture-management strategies: LI95 - when the pasture reached 95% light interception (LI), with 3 paddock-occupation days; and FR - the pasture was managed with a 30-days defoliation interval (DI) and 3 paddock-occupation days. Ten recently calved cows per treatment in year 1 and eight cows per treatment in year 2 were used, i.e. five cows per replicate in year 2 and four cows per replicate in year 2. Intake and digestibility of pasture nutrients, milk yield and milk composition were evaluated. The management strategies imposed on the Tanzania-grass pasture did not affect nutrient intake or digestibility. Milk yield and milk components were not affected by the management strategies, but milk yield per area unit was affected by the use of 95% light interception. The management strategy applying 95% light interception implies greater efficiency in the use of area, which translates to a higher milk yield per unit area, and thus it can be recommended to increase milk yield per area.

Nutrient dynamics under different regimes of stocking and cattle type of temperate pastures in Hokkaido, Japan

AbstractFertilizer recommendations for day and night grazing of milking cattle on land in Hokkaido are based on previous studies of nutrient dynamics. This study confirmed the applicability of recommendations to different grazing conditions and conducted observations to extend the versatility of the fertilizer recommendation. We estimated the dynamics of fertilizer nutrients under various grazing regimes, namely with different cattle types (dairy heifers, beef breeding cows or beef heifers), stocking methods (rotational stocking or continuous stocking), seasons (throughout grass growing season or after harvest of the first crop) and times (24‐hr grazing or time restricted to 5–9 hr/day). Body weight, herbage intake and excretion were measured, and the total intake and return of nutrients to the pasture were calculated. Nutrient removal by grazing, calculated as intake minus return through excretion, increased with herbage intake. Almost all the nutrients removed under grazing throughout the growing season approached the upper or lower limits of our previously reported levels. In many cases, changes in the amounts of soil nutrients with grazing were close to the estimated amounts removed from the soil in the grass growth periods. These results show that the pasture nutrient dynamics quantified in our previous study for lactating cattle are also applicable to dairy heifers and beef cattle and to time‐restricted grazing as well as grazing after 1st cut. Regression equations were obtained to predict the fertilizer recommendations from herbage intakes for these various grazing conditions.

Performance, digestibility, microbial protein synthesis, and body composition of Brangus x Zebu steers on tropical pasture receiving supplementation

The use of additional supplement can affect ruminant performance by increasing the animal weight gain and maximizing profits from the activity. Thus, the objective of this study was to evaluate the influence of protein-energy supplementation on microbial synthesis, animal performance, nutrient digestibility, and body composition of Brangus x Zebu steers on pasture. The experiment lasted 160days and included 36 animals divided into two groups; 18 steers received protein-energy supplementation (PES), and the other 18 received non-supplementation (NPES). Individual pasture intake and nutrient digestibility were estimated using the double indicator technique-chromium oxide and lignin in potassium permanganate. Spot urine samples were collected from 36 animals to determine creatinine, allantoin, and uric acid concentrations. All animals were slaughtered at the end of the experiment to evaluate body composition. There was increase in intake (P < 0.001) and dry matter digestibility (P = 0.01); it resulted in higher animal weight gain (P < 0.001) receiving supplementation. However, there was no difference (P > 0.05) in pasture nutrient intake between treatments. Supplementation increased microbial nitrogen (P < 0.001). For body composition, the model identity test that was applied showed no difference (P > 0.05) between the models, so it was adopted a common equation for both treatments. There was no difference (P > 0.05) for body composition between treatments. Therefore, the use of protein-energy supplementation for steers on pasture allowed higher microbial protein synthesis and better utilization of nutrients, which resulted in better animal performance. The use of protein-energy supplementation at 0.6% BW did not alter the carcass composition of Brangus x Zebu steers. However, it recommends protein-energy supplementation in pasture systems during the rainy period or using an irrigation system.

Impact of Year-Round Grazing by Horses on Pasture Nutrient Dynamics and the Correlation with Pasture Nutrient Content and Fecal Nutrient Composition.

Horse grazing may benefit biodiversity, but the impact of year-round grazing on nutrient dynamics has not been evaluated previously. This study compared pasture quality in a forest-grassland landscape grazed year-round by horses with that in exclosed mown areas. Twelve Gotlandsruss stallions were kept without supplementary feeding in three enclosures (~0.35 horse/ha) outside Uppsala, Sweden, from May 2014 to September 2016. Each enclosure contained three mown exclosures, where grass sward samples were collected monthly and analyzed for chemical composition and vegetation density. Fecal grab samples were collected and analyzed for crude protein (CP) and organic matter (OM) content. There were no differences in exclosure pasture energy or CP content between enclosures (p > 0.05). In grazed areas, there were differences in grass energy and CP content (p > 0.05) between enclosures. During the three summers studied, energy and CP content increased in the enclosures, but decreased in the exclosures. By the end, biomass content/ha was greater in the enclosures than in the exclosures. Fecal OM and CP content showed moderate to strong correlations with pasture nutrient content (r = 0.3-0.8, p < 0.05). Thus, in contrast to monthly mowing, horse grazing diversified pasture chemical composition and increased its nutritive value.

The Use of Mobile Near-Infrared Spectroscopy for Real-Time Pasture Management

Changes in pasture nutrients over the growing season are typically not monitored but doing so may help farmers improve how effectively they utilise forage. The aim of this research was to assess the use of real-time near-infrared spectroscopy (NIRS) for monitoring seasonal changes in nutrient concentrations of different pasture types used for grazing and silage production. Three permanent pastures and three temporary ley pastures (3 years old) grazed by cattle or sheep and/or used for silage production were monitored weekly for 20 weeks from April to August 2017 in the UK. Five pasture samples per field were obtained per week for NIRS analysis and estimation of fresh and dry matter herbage cover (both kg per hectare). Herbage height was also measured each week. Permanent pastures included a diverse range of native UK grass species, and temporary ley pastures were predominantly perennial ryegrass (Lolium perenne) with either white (Trifolium repens) or red clover (Trifolium pretense). Effects of pasture type (permanent or temporary), phase of production (grazed or rested for regrowth) and month of year (April to August) on pasture nutrients (dry matter, crude protein, acid detergent fibre (ADF), neutral detergent fibre (NDF), water soluble carbohydrate (WSC), ash, digestible organic matter (DOMD) and dry matter digestibility (DMD)) were assessed by fitting a linear mixed model. Considerable variation was observed in pasture production and in the concentrations of dry matter, crude protein and WSC in pastures. This study suggests that grazing pastures to a mean height of below 7 cm results in a significantly reduced concentration of crude protein, DOMD and DMD, which may be detrimental to the grass intake and protein intake of the grazing animal. The DOMD and DMD of pasture were positively correlated with herbage height and herbage cover crude protein concentration. An approach of real-time nutrient monitoring will facilitate more timely adaptive pasture management than currently feasible for farmers. This should lead to productivity gain.

Duration-controlled grazing of dairy cows. 1: Impacts on pasture growth, cow intakes and nutrient transfer

ABSTRACTStanding cows off pasture after 4 hours of grazing (duration-controlled (DC) grazing) was researched over 3 years (2008–2011) to compare the losses of nutrients in drainage and surface runoff water with losses under standard grazing (SG). Pasture growth rates, nutrient concentrations and apparent intake by cows were used to model nutrient removals from, and returns to, pasture. Pre- and post-grazing covers and apparent pasture intakes were similar for both treatments. Quantities of N, P and K returned to the DC plots in excreta and effluent were 34%, 0% and 45% less than those returned to SG plots. This reduction in nutrient returns was associated with a nil, 20% and 9% decrease in annual pasture growth on the DC plots in the three respective years. Reductions in annual pasture growth under DC grazing may be avoidable if nutrient removals are balanced with returns in effluent and fertiliser.

The effects of continuous versus rotational grazing on horse pasture nutrient content

Experimental infections are required by current guidelines for investigating the efficacy of anthelmintics in dogs and cats. Recently, alternatives to experimental infections and the sacrificing of research dogs and cats have been evaluated, and novel conceptual investigations and methods of examination have been explored. Several of these approaches could potentially be used in efficacy studies for anthelmintics in dogs and cats. Here, we provide food for thought towards using new tools for evaluating the efficacy of anthelmintics in companion animals, for promoting the value of field trials, and for updating the existing guidelines for the efficacy testing of anthelmintics in dogs and cats.

Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery

On-farm assessment of mixed pasture nutrient concentrations is important for animal production and pasture management. Hyperspectral imaging is recognized as a potential tool to quantify the nutrient content of vegetation. However, it is a great challenge to estimate macro and micro nutrients in heterogeneous mixed pastures. In this study, canopy reflectance data was measured by using a high resolution airborne visible-to-shortwave infrared (Vis–SWIR) imaging spectrometer measuring in the wavelength region 380–2500nm to predict nutrient concentrations, nitrogen (N) phosphorus (P), potassium (K), sulfur (S), zinc (Zn), sodium (Na), manganese (Mn) copper (Cu) and magnesium (Mg) in heterogeneous mixed pastures across a sheep and beef farm in hill country, within New Zealand. Prediction models were developed using four different methods which are included partial least squares regression (PLSR), kernel PLSR, support vector regression (SVR), random forest regression (RFR) algorithms and their performance compared using the test data. The results from the study revealed that RFR produced highest accuracy (0.55⩽R2CV⩽0.78; 6.68%⩽nRMSECV⩽26.47%) compared to all other algorithms for the majority of nutrients (N, P, K, Zn, Na, Cu and Mg) described, and the remaining nutrients (S and Mn) were predicted with high accuracy (0.68⩽R2CV⩽0.86; 13.00%⩽nRMSECV⩽14.64%) using SVR. The best training models were used to extrapolate over the whole farm with the purpose of predicting those pasture nutrients and expressed through pixel based spatial maps. These spatially registered nutrient maps demonstrate the range and geographical location of often large differences in pasture nutrient values which are normally not measured and therefore not included in decision making when considering more effective ways to utilized pasture.