Pasture Management Research Articles

From forest to pastures and silvopastoral systems: Soil carbon and nitrogen stocks changes in northeast Amazônia

The Cerrado-Amazon ecotone has been under intense pressure over the years from agricultural and urban expansion, both of which are land uses that directly affect soil quality. The objective of this work was to evaluate the responses of soil carbon and nitrogen stocks, soil organic matter (SOM) quality, carbon isotopic composition (13C) from C3 and C4 plants and carbon sequestration and emission to silvopastoral systems, natural vegetation (NV), fallow pasture (FP), and intensive management pasture (IMP). Silvopastoral systems had different shading levels: 25 % (SP25), 50 % (SP50) and 75 % (SP75). The grass cultivated in all treatments was Megathyrsus maximus cv. Mombaça. The experimental design consisted of four replicates, and collection sites were distributed in strips throughout the study areas at different soil layers (0.00–0.05; 0.05–0.15; 0.15–0.30; 0.30–0.60 and 0.60–1.0 m deep). The conversion of natural vegetation in FP areas and silvopastoral systems (SP25 and SP75) led to increases in total C and N stocks (up to 1.0 m) when compared to other land use systems (SP50, IMP, and NV), which did not occur with total labile-C and C-POM. FP, SP25 and SP75 significantly increased labile C stocks in MAOM found in the 0.30–0.60 m layer. A greater enrichment of 13C in MAOM was observed with increasing depth for silvopastoral systems (SP25, SP50 and SP75) and NV. C-MAOM stocks derived from C3 plants were higher in soils under SP25 and SP75 and from C4 plants under FP. C-POM stocks were higher in all silvopastoral systems under study. IMP and FP affected δ13C values in MAOM and POM, especially in the 0.00–0.05 m layer. C sequestration increased under FP and SP25, with greater contributions from C4 and C3 plants, respectively. Of the silvopastoral systems, SP25 had the highest C stock in soil and contributed to the sequestration of 1.67 Mg C ha−1 yr−1.

12 Tools for Climate-Smart Decision Making for the Stocker/Feedlot Sector

Abstract There is an increased interest in the use of climate-smart practices and technologies to be more effective in climate change mitigation. These practices include grazing management, pasture management, animal and herd management, animal disease and health, animal breeding, and supplemental feeding during the stocker phase. In the feedlot sector, climate-smart practices range from supply chain challenges including climate smart adaptation by crop producers, cattle type, manure management, health management, and use of growth promoting technologies. There are certainly other technologies not mentioned and are not meant to be marginalized. During the stocker phase, grazing management is highly variable and dependent on climatic conditions and variability, plant species and plant health, water management, soil health, and cattle type. A correct balance among the different land managers, government agencies, and recommendations from university researchers and Extension personnel should optimize these practices. For instance, there is a focus on rotational grazing to increase the efficiency of grazing management. However, in the southwestern U.S. this practice may not be practical given the vast area and landscape used in production. In certain geographical areas, pasture management is becoming increasingly important as decreasing water sources (particularly aquafer levels) are apparent. Some data suggests that now is the time to change from cereal crop production to perennial forages while water is still available to establish the forages. This, in turn, may improve soil health and carbon sequestration. However, as with all mitigation strategies, this may decrease supply of cereal grains available to small feedlot producers, albeit a significant amount of cereals are transported to the cattle feeding sector. Animal breeding programs to improved production, heat tolerance, disease resistance, and improved reproduction is important. As with any production practice, supplementation programs may become necessary under certain situations. It is well known that cattle treated for respiratory disease have poorer performance and carcass characteristics than cattle not treated. Use of growth promoting technologies improve animal performance and decreases total methane produced. No doubt manure production from feeding operations produce methane. Dairy and swine operations can harvest the methane and with use of biogas collectors flare off the methane or produce energy for electric generators, however, this is not practical under feedlot situations; however, application of manure to land may improve soil organic matter. Building resilience in evaluating climate-smart practices will satisfy multiple objectives including impacting food security without many constraints to adoption.

2 Satellite-Based Decision Support Tools to Assist Grazing Cattle Production

Abstract Grasslands cover approximately 40.5% of the surface of the earth and 80% of agriculturally productive land. After forests, grasslands are the primary carbon sink source and the most used feed source for livestock production. Regularly monitoring grasslands assures efficient management and sustainability of pasture-based production systems. Conventional ground-based methods to monitor grassland production and management rely on field measurements, which are time-consuming and usually restricted to small-scale assessment. Using satellite information allows for large-scale monitoring of grasslands and capturing the spatial variability of the land surface with high temporal resolution. Various methods for grassland monitoring based on satellite data can be applied, such as classifications, correlations/regression analyses, and time series analyses. Depending on the purpose of the application, these methods are sometimes combined to derive grassland management and production information. The ability of satellite-based data to quantify vegetation characteristics depends on the type of sensor and instrumentation features, such as spectral, radiometric, spatial, and temporal resolution, polarization, and angularity. The models to estimate grassland biomass based on remote sensing have been chiefly focused on optical systems. The spectral reflectance of raw bands and vegetation indices were used as proxies to investigate spatial and temporal patterns of grassland production. Optical (multispectral or hyperspectral) sensors are passive and require sunlight, so they depend highly on the weather (cloud) and light conditions. Thus, there has been increasing interest in active sensors, such as Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LIDAR) sensors, which are not constrained due to clouds but are more complex. Using satellite data in combination with field measurements has commonly yielded regression models (e.g., linear, power, logarithmic, multiple linear) for estimating grassland biomass or biophysical characteristics (e.g., chlorophyll, leaf area index) of different types of grasslands. The exponential evolution of digital computers has pushed forward machine learning-based regression methods to estimate biomass. Random forest, support vector machines, and artificial neural networks are the most used algorithms. The possibility of accurate mapping and monitoring of biomass and nutritional attributes of grasslands based on satellite provides essential insights into the decision support system for pasture management. A better understanding of the nitrogen status of pastures, forage biomass, and its nutritive value is instrumental in livestock and forage management. Timely prediction of these variables can help improve decision-making by grazing land managers on, for instance, the adjustment of stocking rate or adequate supplementation to match the needs of animals toward more sustainable production. Future use of satellite-based grazing models in tandem with ruminant nutrition models will enable to development of decision-support tools to assist with many aspects of livestock production in diverse environmental conditions and accounting for temporal variability. (FAPESP #2020/14367-7)

Using diagnostics in supporting sustainable worm control in horses

Anthelmintic resistance in horses is an ongoing problem. Changes in methods of worming – from group worming to testing individual horses and worming appropriately – are important to help prevent any further resistance developing. There are various methods to test for worm burdens in horses, including faecal egg counts, blood tests and saliva tests. This article discusses these methods in relation to specific species of gastrointestinal parasites, in addition to how pasture management can be used to help maintain low levels of resistance.

On-farm assessment of grazing behaviour of dairy cows in two pasture management systems by low-cost and reliable cowtrackers

In dairy farming, sensors are commonly applied to monitor animal health and performance. One key task of such sensors is the behaviour classification of cows. The behaviour classification needs to take into account the differences between stable and pasture: the process of feed intake by grazing is different from feed intake in the stable. Pasture management systems differ and this introduces further variability within pasture-based systems, e.g. by different sward heights. Moreover, on pasture, spatial variables like walking distance allow the inference of further information about cow behaviour. Therefore, data accessibility and customizability of sensors is important for the development of pasture specific behaviour classification algorithms. Our objective was to construct an open-source and neck located cowtracker that combines an accelerometer, gyroscope and global navigation satellite system sensors with an Arduino microcontroller. The blueprints of the cowtracker are provided in the manuscript. Cows were tracked on two commercial dairy farms with different breeds and in different grazing systems to ensure high variability of the data. Animal behaviour was classified by training a random forest algorithm that was used to predict animal behaviour from the sensor data. The training data was obtained from visual observations in the field and differentiating between the behaviours grazing, rumination, walking, resting and drinking. The overall accuracy was 0.94 when predicting test data consisting of data retained randomly from all individuals. The predictive performance slightly decreased to 0.89 overall accuracy when whole animal data sets were retained as test data. The decrease in predictive performance was stronger for the rarely observed behaviour drinking. As a showcase, cow behaviour was analysed and differed between the two grazing systems. In continuous grazing at low sward heights (6 cm) the cows allocated more time to grazing and walking whereas in rotational grazing with pre- and post-grazing sward heights of 11.3 cm and 6.3 cm respectively, the cows allocated more time to rumination and less to grazing. The observed grazing time was comparably short in this experiment. The spatial observations revealed longer walking distances and higher herd scatter in continuous grazing which reflects the greater effort in satisfying the feed demand in low swards.

Rest–grazing before soil thawing alters the below‐ground distribution and extent of rooting in Carex subalpine meadows

AbstractLivestock activities impact plant rhizomes and roots in the topsoil, particularly in the early stages of spring for a Carex subalpine meadow. Rest–grazing during critical periods can prevent harm to these roots and rhizomes caused by livestock, thereby enhancing the ecological performance of grasslands. In accordance with soil thawing and plant re‐greening, we designated five rest–grazing periods during the spring in a Carex subalpine meadows. The findings demonstrate that the topsoil of 0–10 cm, largely consisting of Carex roots, housed over 70% of root volume and root mass. After the plant had regained its greenery for a certain duration, rest–grazing resulted in higher vertical root length distribution index. With earlier rest–grazing, root growth in deeper soil was promoted, consequently transporting more organic material to the deeper soil. Deferred rest–grazing time has the potential to enhance lateral rooting extent, root resistance to vertical uprooting, and root accumulation in topsoil. It follows that increased root accumulation in the topsoil leads to greater loss of soil nutrients and carbon. Consequently, we recommend rest–grazing prior to soil thawing for Carex subalpine meadows, which encourages Carex root growth and fosters greater biomass accumulation in deeper soils. Avoiding “running for the green” and “trampling tillage” has a positive effect on both productivity and long‐term soil nutrients and carbon pool management in pastures.

PSIV-3 Biofumigation to Reduce Swine Parasite Contamination on Organic Pastures

Abstract Parasite infection is a common problem in organic pig production. Pastures grazed by infected pigs can become sources of parasite transmission because parasite eggs can survive in soil for several years. Biofumigation is a method used by farmers to reduce plant pathogens and nematodes in soil by incorporating plant residues into the soil that rapidly release toxic isothiocyanates. This study explored whether grazing crops with biofumigation properties affects parasite fecal egg counts of pigs managed organically, and subsequent parasite eggs in organic pasture soil after biofumigation. A study was conducted using 66 pigs (BW = 72.6 kg ± 21.2 SD) that were raised organically in two blocks. Within each block, pigs were housed in four pens (8 to 9 pigs/pen) and provided feed ad libitum in a hoop barn. Each pen had access to a pasture (0.5 acres). Each pasture was subjected to one of four management treatments: control pastures (Ladino clover) or biofumigation pastures (Rapeseed), each with or without incorporating plant residues. Each pasture was split into four paddocks (9.1m × 10.7m) and pigs were given access to each paddock for one week. Fecal samples (n = 198) were collected from each pig before, two and four weeks after grazing. Triple soil samples (n = 480) were collected from each paddock before grazing, immediately after pigs were removed from pasture paddocks, and weekly thereafter for three weeks. Fecal and soil samples were analyzed for Ascaris suum, Trichuris suis, and Oesphagostomum spp. eggs. Data were analyzed using a mixed model with pen or paddock as the experimental unit. Fecal egg counts of the three parasite species did not differ between pigs that grazed on pastures with different management treatments. All pigs had drecreased Ascaris suum (P < 0.01) and Trichuris suis (P < 0.01) fecal egg counts after four weeks of grazing compared with before grazing. Both clover and rapeseed pastures without incorporating plant residue had decreased Ascaris suum egg counts in the soil compared with incorporating plant residue (both P < 0.05). Rapeseed pastures without incorporating plant residue had reduced (P < 0.05) Trichuris suis egg counts in the soil compared with clover pastures but did not differ from rapeseed pasture with incorporated plant residue. Across pasture management treatments, Ascaris suum and Trichuris suis eggs in the soil were reduced (P < 0.05) two and three weeks after pigs were removed from pastures compared with before, immediately after, and one week after pigs were removed. These results suggest that grazing pastures crops with biofumigation properties did not affect fecal egg counts of pigs. While the number of parasite eggs decreased overtime in pasture soil, biofumigation may not be an effective way to reduce swine parasite eggs in organic pastures.

Tradycja i innowacyjność w ochronie środowiska przyrodniczego regionów górskich

Aim of the study: Pastoral management in Europe is an important element in protecting biodiversity and the landscape. Shepherding stimulates the development of the market of traditional and regional products, handicrafts and services. Pastoral farming in Europe is an important element of biodiversity and landscape protection. Shepherding stimulates the development of the market for traditional, regional products, handicrafts and services. For many years, wool was an important product obtained in mountain areas from local sheep breeds, now treated as a waste product. The aim of the study is to describe farming methods in mountain areas, indicating innovative possibilities of using sheep wool. Material and methods: The material consisted of literature studies and own research on management in mountain areas. Own research and literature analyzes have shown that, due to its properties, wool obtained from mountain sheep can be used as a natural fiber in a biodegradable composite and constitute an alternative to plastic packaging. The introduction of natural fibers in the fertilizer composite can improve not only soil fertility but also bring economic effects, such as the use of wool of Polish mountain sheep as part of the circular economy. Results and conclusions: Summarize, the search for alternative directions for the use of sheep wool is of strategic importance for maintaining and even increasing the sheep population in Poland and in other European Union countries. Occurring soil droughts in the summer period affect the reduction of plant yields, and for this reason, the application of biodegradable biocomposite to the soil will not have a negative impact on the environment of mountain pastures and meadows.

Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review

The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture.

Agropastoralism and re-peasantisation: the importance of mobility and social networks in the páramos of Boyacá, Colombia

The páramos of Boyacá in Colombia are earmarked for delimitation to prevent the expansion of the agricultural frontier and protect endemic flora that contribute to water provision for cities. A varied conservation toolbox will be used, including the creation of protected areas for re-wilding and the ‘sustainable’ transitioning of livelihoods identified as environmentally destructive. Agriculture and cattle livestock farming has been identified for transitioning. Despite the negative discourse related to livestock holding, this paper argues that small-scale agropastoralism contributes to re-peasantisation and provides the foundations for an agrobiodiverse conservation approach. Agropastoralism facilitates re-peasantisation through strong socio-economic networks, interconnected communities, the solidarity economy, and self-management of natural resources. Whilst, agropastoral mobility spatially binds social networks across large and disconnected spaces. Mobility is also fundamental to dynamic land access and pasture management, as it prevents over-grazing. This exemplifies how resilient socio-economic networks and mobile production strategies could be harnessed for agrobiodiversity, instead of land sparing and other sedentary ‘green’ economies. This paper makes conceptual contributions to ‘autonomy’ in re-peasantisation by empirically demonstrating the importance of mobile and flexible systems of production. It also makes a novel methodological contribution in applying a spatial lens that further unpacks how movement across the páramos facilitates autonomy and re-peasantisation. These themes are explored using interview data from 53 semi-structured interviews with key stakeholders and small-scale agropastoralists from across the páramos and field observation. The paper concludes by recommending a harnessing of agropastoral knowledge, to potentiate agrobiodiversity, for a more socio-ecologically just approach to farming and conservation in the páramos.

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.

Pasture lands of Kazakhstan: rational use

The goal is to find ways to solve problems of pasture use in Kazakhstan using GIS technologies; conduct monitoring of pastures based on remote sensing of land (ERS); identify the condition of pasture lands using the NDVI index. Methods – analytical, economic-static, monographic, abstract-logical. Results – space monitoring was carried out, their degradation was assessed using remote sensing based on images received from the Planet satellite through the portal https://www.planet.com/. It has been established that pastures in the category of agricultural lands account for 45.1%, reserve lands 66.6 million hectares, which can be considered as a reserve for agricultural use. The cultural and technical level of pasture lands is characterized by the indicators of overgrazing, stoniness, forest cover and bush cover. Based on the research, it was concluded that used pasture lands are located mainly around rural settlements (Rural Settlements). Grazing of livestock on remote and distant pastures is impossible due to the lack of pasture infrastructure. As a result, pasture lands near the RS are under excessive pressure, which leads to a reduction in the number of livestock and, accordingly, livestock products. The analysis showed that since 2012, the total area of pasture land in reserve lands has decreased by 20 thousand hectares and increased in the category of agricultural land. Conclusions – pasture land use requires a revision of the institutional framework for pasture management. Competence on these issues must be transferred from district executive bodies to local government. It has been determined that due to non-compliance with animal grazing standards, pastures are trampled, resulting in a decrease in their productivity and a decrease in size. An effective tool is a system of remote monitoring of pastures, combined with ground surveys of land. This, in turn, allows us to objectively assess the condition of pasture lands at various scales. The republic has a huge potential of natural pastures, which is not sufficiently used to create a sustainable food supply and obtain environmentally friendly and cheap livestock products.

Long-term impacts of conservation pasture management in manuresheds on system-level microbiome and antibiotic resistance genes.

Animal manure improves soil fertility and organic carbon, but long-term deposition may contribute to antibiotic resistance genes (ARGs) entering the soil-water environment. Additionally, long-term impacts of applying animal manure to soil on the soil-water microbiome, a crucial factor in soil health and fertility, are not well understood. The aim of this study is to assess: (1) impacts of long-term conservation practices on the distribution of ARGs and microbial dynamics in soil, and runoff; and (2) associations between bacterial taxa, heavy metals, soil health indicators, and ARGs in manures, soils, and surface runoff in a study following 15 years of continuous management. This management strategy consists of two conventional and three conservation systems, all receiving annual poultry litter. High throughput sequencing of the 16S ribosomal RNA was carried out on samples of cattle manure, poultry litter, soil, and runoff collected from each manureshed. In addition, four representative ARGs (intl1, sul1, ermB, and blactx-m-32) were quantified from manures, soil, and runoff using quantitative PCR. Results revealed that conventional practice increased soil ARGs, and microbial diversity compared to conservation systems. Further, ARGs were strongly correlated with each other in cattle manure and soil, but not in runoff. After 15-years of conservation practices, relationships existed between heavy metals and ARGs. In the soil, Cu, Fe and Mn were positively linked to intl1, sul1, and ermB, but trends varied in runoff. These findings were further supported by network analyses that indicated complex co-occurrence patterns between bacteria taxa, ARGs, and physicochemical parameters. Overall, this study provides system-level linkages of microbial communities, ARGs, and physicochemical conditions based on long-term conservation practices at the soil-water-animal nexus.

Prediction of pasture yield using machine learning-based optical sensing: a systematic review

AbstractAccurate and reliable predictions of biomass yield are important for decision-making in pasture management including fertilization, pest control, irrigation, grazing, and mowing. The possibilities for monitoring pasture growth and developing prediction models have greatly been expanded by advances in machine learning (ML) using optical sensing data. To facilitate the development of prediction models, an understanding of how ML techniques affect performance is needed. Therefore, this review examines the adoption of ML-based optical sensing for predicting the biomass yield of managed grasslands. We carried out a systematic search for English-language journal articles published between 2015-01-01 and 2022-10-26. Three coders screened 593 unique records of which 91 were forwarded to the full-text assessment. Forty-three studies were eligible for inclusion. We determined the adoption of techniques for collecting input data, preprocessing, and training prediction models, and evaluating their performance. The results show (1) a broad array of vegetation indices and spectral bands obtained from various optical sensors, (2) an emphasis focus on feature selection to cope with high-dimensional sensor data, (3) a low reporting rate of unitless performance metrics other than R2, (4) higher variability of R2 for models trained on sensor data of larger distance from the pasture sward, and (5) the need for greater comparability of study designs and results. We submit recommendations for future research and enhanced reporting that can help reduce barriers to the integration of evidence from studies.

Management impacts on organic carbon under continuous perennial grass, perennial grass-legume mixture, and annual cereals on a thick Black Chernozemic soil

Impacts of annual and perennial pasture management on soil organic carbon (SOC) and equivalent SOC stocks (equal soil mass basis) were investigated in two trials [CAESA (1994–1997) and BMP (2008–2012) trials] conducted on the same experimental paddocks at Lacombe, AB. The original site was broken from perennial grass in 1992, and the CAESA trial established in 1993. Between 1994 and 1997, half of the paddocks included winter triticale and a mixture of triticale and spring barley; half included smooth and meadow bromegrass; and each paddock was light, medium, or heavily grazed. The BMP trial (2008–2012) on the same paddocks included fertilized, direct seeded barley as silage; grazing and haying of unfertilized meadow bromegrass, fertilized meadow bromegrass, and meadow bromegrass and alfalfa mixture; and unfertilized oldgrass that was continuous since 1994. Between trials (1998–2007), all paddocks received no fertilizer. In the 0–15 cm depth, SOC under oldgrass was constant between 1994 and 2012 and averaged 88 Mg C ha−1. Under barley silage, SOC decreased from 89 to 72 Mg C ha−1 by 2012. Between 1994 and 2012, SOC decreased in all treatments re-established on original annual forage (1994–1998) but not to the level of barley silage. Light fraction carbon was the highest under oldgrass and the lowest under barley silage. Overall, oldgrass with no fertilizer inputs maintained a constant SOC, although annuals reduced SOC stocks. Re-establishment of perennial grass with grazing may therefore reduce SOC loss, whereas haying perennial grass may not reduce SOC loss.

Grazing and precipitation addition reduces the temporal stability of aboveground biomass in a typical steppe of Chinese Loess Plateau

Few studies on the effects of human activities and global climate change on temporal stability have considered either grazing or precipitation addition (PA). How community stability responds to the interaction between PA and grazing in a single experiment remains unknown. We studied the impact of grazing and PA on the temporal stability of communities in four years field experiment conducted in a typical steppe, adopting a randomized complete block design with grazing was the main block factor and PA was the split block factor. Grazing and PA had negative impacts on the temporal stability of communities. PA reduced the community stability through decreasing the stability of subordinate and community species richness (SR), whereas grazing reduced the community stability through decreasing the stability of the SR and dominant species. In contrast, grazing and PA maintained community stability through increasing species asynchronism and promoting the decoupling of asynchronism and stability. Our results revealed the different mechanisms of grazing and PA on community stability. Exploring the response characteristics of population dynamics to global climate change and pasture management is key to understanding future climate scenarios and changes in community stability under grazing.

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

Milk production plays an essential role in the global economy. With the development of herds and farming systems, the collection of fine-scale data to enhance efficiency and decision-making on dairy farms still faces challenges. The behavior of animals reflects their physical state and health level. In recent years, the rapid development of the Internet of Things (IoT), artificial intelligence (AI), and computer vision (CV) has made great progress in the research of precision dairy farming. Combining data from image, sound, and movement sensors with algorithms, these methods are conducive to monitoring the behavior, health, and management practices of dairy cows. In this review, we summarize the latest research on contact sensors, vision analysis, and machine-learning technologies applicable to dairy cattle, and we focus on the individual recognition, behavior, and health monitoring of dairy cattle and precise feeding. The utilization of state-of-the-art technologies allows for monitoring behavior in near real-time conditions, detecting cow mastitis in a timely manner, and assessing body conditions and feed intake accurately, which enables the promotion of the health and management level of dairy cows. Although there are limitations in implementing machine vision algorithms in commercial settings, technologies exist today and continue to be developed in order to be hopefully used in future commercial pasture management, which ultimately results in better value for producers.

Cradle-to-grave environmental analysis of an alpaca fiber sweater produced in Peru

Animal fibers are an important raw material for the fashion industry but have recently been discussed due to the environmental impacts related to their production. In order to provide scientific information for decision-making in the Peruvian alpaca sector a cradle to grave carbon footprint of one (01) wear of a 100 % alpaca fiber sweater has been conducted. For the modeling of the fiber procurement stage primary data regarding livestock management and annual production parameters were obtained from interviews with 42 Peruvian alpaca herders from the main producing regions in South and Central Peru. Data for the processing stages (spinning and dyeing, knitting and weaving) were collected by means of interviews and questionnaires from three alpaca fashion companies in Arequipa and Lima. The distribution, use, and end-of-life stages were modeled with secondary data. The resulting carbon footprint of one wear of the alpaca fiber sweater is 0.449 kg CO2 equivalents (CO2e). Most emissions occur during the lifecycle stages of fiber production and distribution (70 % and 14 % of CO2e emissions, respectively). Methane emissions from enteric fermentation account for 87 % of the impact within the fiber procurement stage. The environmental impacts during the distribution stage were dominated by retailing and road transport in the destination countries and export by air and sea (53.1 % and 46.4 % of carbon emissions in this stage, respectively). Other life cycle stages were found to be less relevant emission sources. The study concluded that the main strategies for impact mitigation should focus on improving the efficiency of the fiber procurement systems. Furthermore, several knowledge gaps have been identified and should be addressed by future research regarding methane emissions associated with the main co-products of the livestock systems, ecosystem services in the Andes and especially Andean wetlands and potential mitigation strategies of greenhouse gases related to different pasture management options.

A Comparative Analysis of Carbon Footprint in the Andalusian Autochthonous Dairy Goat Production Systems.

The small ruminant livestock sector faces the challenge of reducing greenhouse gas (GHG) emissions. Carbon footprint (CF) studies on dairy goats, the most widely used indicator to quantify the impact of livestock farming on global warming, are still few. The aim of this study was to calculate the CF of the different production systems of autochthonous dairy goat breeds presently occurring in Andalusia (S Spain) and identify systems and practices that can minimize their environmental impact in these terms. Twenty-one farms were monitored during a year, obtaining valuable information that allowed the CF calculation on a "cradle-to-gate" approach, taking into account both GHG emissions at the farm level and carbon sink by vegetation associated with land-based systems. Results showed similar CF values for the analyzed systems (1.42, 1.04, 1.15, and 1.17 kg CO2-eq kg-1 fat-protein corrected milk for indoor systems without associated crops, indoor systems with associated crops, grazing systems with high feed supply, and pastoral systems, respectively). To minimize their environmental impact, specific actions must be developed for each system, particularly regarding genetic improvement, reproductive and feeding management, including pasture management, and the integration of livestock activity into the bio-circular economy with the help of professional advice.

Short-term exposure of dairy cows to pyrrolizidine alkaloids from tansy ragwort (Jacobaea vulgaris Gaertn.): effects on health and performance

ABSTRACTThe increasing spread of ragworts is observed with concern. Ragworts like tansy ragwort (Jacobaea vulgaris Gaertn.) or marsh ragwort (J. aquatica) contain pyrrolizidine alkaloids (PA) which may induce hepatotoxic effects. Grazing animals usually avoid ragworts if their pasture management is appropriate. Preserved feed prepared from ragworts contaminated meadows may, however, lead to a significant exposure to PA. Previous studies on toxicity of PA for dairy cows revealed inconsistent results due to feeding ragwort plant material which was associated with heterogeneous PA exposure and thus failed to conclusively deduce critical PA doses. Therefore, the aim of the present study was to expose dairy cows (n = 4 per group) in a short-term scenario for 28 days with increasing PA doses (PA1: 0.47 mg PA/kg body weight (BW)/day (d); PA2: 0.95 mg PA/kg BW/d; PA3: 1.91 mg PA/kg BW/d) via oral administration by gavage of a defined PA-extract. While group PA3 was dosed with the PA-extract alone, groups PA2 and PA1 received PA-extracts blended in similar volumes with molasses to provide comparable amounts of sugar. Additionally, two control groups were treated either with water (CONWater) or with molasses (CONMolasses) to assess the effects of sugar without PA interference. While clinical traits including dry matter intake, milking performance, rectal body temperature, ruminal activity and body condition score (BCS) were not influenced by PA exposure, activities of enzymes indicative for liver damages, such as gamma-glutamyltransferase (GGT), aspartate aminotransferase (AST) and glutamate dehydrogenase (GLDH), increased significantly over time at an exposure of 1.91 mg total PA/kg BW/d.