A High Cattle-Grazing Density Alters Circadian Rhythmicity of Temperature, Heart Rate, and Activity as Measured by Implantable Bio-Loggers

Six cows managed under extensive grazing conditions were used to study the effect of moving the animals to a higher grazing density on the circadian rhythms of temperature (T), heart rate (HR), and activity (ACT), which were recorded by implantable bio-loggers. Cows were maintained at a density of 1.5 livestock units per hectare (LSUs/ha; low density, LD) until they were moved to a grazing area at 128 LSUs/ha (high density, HD). Animals were implanted subcutaneously with a T, HR, and ACT bio-logger, which was programmed to record data at 5-min intervals. For each animal, cosinor rhythmometry (the study of circadian rhythms by fitting a sine wave to a time series) was applied to the data recorded over 5 days in LD and HD. Mean Midline Estimating Statistic of Rhythm (MESOR; the average value around which the variable oscillates), amplitude (difference between the peak and the mean value of a wave), and acrophase (timing of peak activity) were calculated and evaluated statistically. Differences between mean day and nighttime values, and mean LD and HD values were calculated. Cows presented cosinor curves that fit a 24-h rhythm (p < 0.001) in T, HR, and ACT at both densities. MESOR (T: 37.98 vs. 38.02°C; HR: 69.12 vs. 65.91 bpm; ACT: 49.39 vs. 40.41 mg, for LD and HD, respectively) and amplitude (T: 0.28 vs. 0.28°C; HR: 4.12 vs. 3.14 bpm; ACT: 18.14 vs. 11.28 mg, respectively) did not differ significantly between the two densities; however, significant (p < 0.05) differences between densities occurred in the acrophase of the three variables; specifically, the T acrophase was 2 h later at HD (22:45 h) than LD (20:45 h), and HR (LD: 19:51; HD: 16:49 h) and ACT acrophases 3 and 2 h earlier at HD than LD (LD: 14:47; HD: 12:49 h), respectively. T and ACT differed significantly (p < 0.01) between daytime (mean ± SE; 37.92 ± 0.19°C, 40.39 ± 4.74 mg) and nighttime (38.14 ± 0.17°C, 29.93 ± 5.66 mg). In conclusion, our study suggests that a high animal grazing density might exacerbate the social competence for valuable resources for animals, resulting in shifting the circadian rhythmicity of temperature, heart rate, and activity of the cows, advancing or delaying their acrophases.

Exploring the past of Mavrovouni forest in the Pindus Mountain range (Greece) using tree rings of Bosnian pines

Key message Long Bosnian pine chronologies from different mountains are shaped by different climatic parameters and can help identify past drought events and reconstruct landscape histories. Abstract We developed a 735-year-long Pinus heldreichii chronology from the southern distribution limit of the species, expanding the available database of long Bosnian pine chronologies. Tree-ring growth was mainly positively correlated with growing degree days (GDD: r1950–2018 = 0.476) while higher temperatures during both winter and growing season also enhanced growth (TWT: r1950–2018 = 0.361 and TGS: 0.289, respectively). Annual precipitation, during both calendar and water years, had a negative but weaker impact on annual tree growth. The newly developed chronology correlates well with chronologies developed from the neighboring mountains. The years with ring width index (RWI) lower than the average were found to correspond to cool years with dry summers. Still, the newly developed chronology was able to capture severe drought events, such as those in 1660, 1687, and 1725. Several old living trees had internal scars presumably caused by fires. Therefore, old mature trees could be used for fire history reconstruction in addition to climate reconstruction. Although the presence of lightning scars indicates an important natural agent of fire ignition, human activities associated with animal grazing could also be an underlying reason for fires in the region.

Teasing Apart Silvopasture System Components Using Machine Learning for Optimization

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels <0.04 mg kg−1 (126% greater grazing hours per AU), soil Cr <0.098 mg kg−1 (108%), and a SAGA wetness index of <2.7 (57%). Cattle also preferred grazing (88%) native grasses compared to orchardgrass (Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

A Forest Future?

The other climate benefit of no longer eating meat is that it will make available huge swathes of new land for afforestation—the return of forests to land that has not recently been forested. One consequence of the inverted energy returned on energy invested (EROI) of meat is that we use far more land for farming than we would need if our diet were to be exclusively plant based. In the United States alone, somewhere in the region of 834 million acres could be made available through this strategy, much of it suitable for afforestation. Even the afforestation of land not currently used for farming has the potential to reduce global carbon dioxide emissions by nearly one-third. Adding in land currently dedicated to animal grazing and feed crops is a potentially game-changing development in the fight against climate change.

Beetles and Meteorological Conditions: A Case Study

The meteorological factors study in the beetle population dynamics, as well as its association with vegetation, is of fundamental importance for understanding the variation that occurs in its population. Thus, it was reported the influence of temperature, humidity, insolation and precipitation on the beetles in general and it was presented a case study that examined the relationship between time and population fluctuation of curculionids in Mata de Cocal and an area used for crop rotation and animal grazing, in the city of Teresina, Brazil, from August 2011 to July 2012. It was verified that beetles populations certain are governed and conditioned by meteorological variables to a greater or lesser extent depending on the characteristics of the community itself and the biotic and abiotic environmental factors of the area where they live: the temperature that changes the its metabolic rate, the insolation and humidity that can affect its fertility and longevity can be cited as examples. From the case presented, It was found that the Curculionidae community has a positive association with precipitation and humidity and a negative association with insolation and temperature, being that in native forests curculionids are not as dependent on meteorological variables as in agricultural fields.

Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

Quantifying Grass Coverage Trends to Identify the Hot Plots of Grassland Degradation in the Tibetan Plateau during 2000–2019

Grassland covers 54% of the Tibetan Plateau (TP) and suffered overgrazing and degradation problems during past decades. To alleviate these problems, a series of policy measures have been implemented during recent two decades and inevitably caused changes of the grassland. To this end, this study quantitatively analyzed the grassland changes and the effects of reduced grazing intensity, and identified the hot plots of grassland degradation in the TP during 2000–2019. The grassland status was indicated by the Fractional Vegetation Cover in the green grass period (GP), i.e., FVCGP, and its changes and spatial variations were detected by analyzing the FVCGP trends and their distribution, using the Mann–Kendal, Sen’s Slope, and ArcGIS buffering methods, and data of the MOD13Q1 Collection 6 products and other sources. The results showed that 62.12% of the grasslands were significantly increased in the FVCGP, and 28.34% had no apparent changes. The remaining 9.54% of the grassland significantly decreased in the FVCGP, mainly occurring in the areas nearby roads, rivers, and lakes, and distributed mostly in a point pattern. Of the total FVCGP decreased grassland area, 27.03% was clustered and identified as the hot plots of grassland degradation in six main regions. Decreased grazing intensity and increased precipitation contributed to the increase of grassland FVC in the TP, while local overgrazing could be the main cause of the FVC decrease. To strength the grassland restoration in the TP, the government supports and supervision should be enhanced to further mitigate the grassland pressure of animal grazing, particularly in the hot plot areas of degradation.

Helminth biodiversity and spread peculiarities of helminthiasis of domestic artiodactyls in the Caspian Depression ecosystems

The Caspian Depression is situated on the flat eastern territory of the North Caucasus. According to the soil and climatic conditions, different ecosystems are neighboring in the Caspian Depression: lowland moistened areas (up to 30% of the territories), plat steppes (more than 50%), salt marshes (up to 10%), and semi-deserts (up to 10%). The majority of the Caspian Depression territories are used for domestic animal grazing, where more than 3 million sheep, goats and up to 150 thousand heads of cattle are contained. 46 nosological units of helminthiasis agents are parasitized on domestic ruminants, including 37 species of sheep and 38 of cattle. The average temperature in winter is up to +12°С; in summer – up to +45°С (210-220 days a year). This region is characterized by year-round use of land and an increase in zootechnical standards for keeping animals per unit of pasture (up to 8 heads of sheep and 3 heads of cattle). Moreover, it has a rich variety of pathogens of helminthiasis, the similarity of most types of helminths for domestic ruminants, a high number of invasive stages in biotopes, and up to 95% of the prevalence of mixed invasions of dangerous parasitoses.