Search Results (6)

The relationship between plant productivity, measured according to biomass and species richness, is a fundamental focal point in community ecology, as it provides the basis for understanding plant responses or adaptive strategies. Although studies have been conducted on plant biomass and environmental factors, research concerning mountainous grassland areas is scarce. Therefore, the aim of the present study was to examine the influence of environmental factors on aboveground plant biomass in the mountainous grassland of the Mountain Zebra National Park, South Africa. Biomass distribution was uneven within the park, owing to certain species having relatively higher biomass values. These differences may be attributed to the chemical and physical properties of the soil, including carbon and nitrogen content, soil pH, and soil texture (sand, silt, and coarse fragments). A disc pasture meter was used to collect biomass data. Multiple regression analysis revealed that most environmental factors did not significantly influence plant biomass. The only environmental factor influencing plant biomass was soil pH; the influences of other factors were not statistically significant. The results of this study elucidate the interactions of environmental factors with plant biomass. Future research could investigate how environmental factors influence plant biomass, both below and above the ground in mountainous grassland. Full article

In large hayfields belonging to intensive dairy systems, satellite remote-sensing data can be useful to determine the hayfield yield and quality efficiently. In this study, we compared the land survey data of hayfield yield, and its quality parameters such as crude protein and neutral detergent fiber digestibility (NDF), with the Sentinel-2 satellite image data for thirteen hayfield paddocks in Kamishihoro region, Hokkaido, Japan. Commonly used indices derived from the satellite image data, including the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were used to assess the hayfield yield and quality. In this region, hayfields are usually harvested twice yearly, in early summer (first harvest) and late summer (second harvest). As result, the Sentinel-2 data could predict the pasture growth and quality for the first harvest better than those for the second harvest. The EVI and the index based on the bands B8a and B7 were the best predictors for the biomass and NDF for the first harvest, respectively. However, the satellite-image-based predictors were not found for the second harvest. Towards the second harvest season, the color of the hayfield surface became more heterogeneous because of the flowering of weeds and uneven pasture growth, which made it challenging to predict pasture growth based on the remote-sensing data. Our land survey approach (quadrat-based sampling from a small area) should also be improved to compare the remote-sensing data and the pasture with uneven growth. Full article

Parthenium weed (Parthenium hysterophorus L. (Asteraceae)), native to the Americas, is in the top 100 most invasive plant species in the world. In Australia, it is an annual weed (herb/shrub) of national significance, especially in the state of Queensland where it has infested both agricultural and conservation lands, including riparian corridors. Effective control strategies for this weed (pasture management, biological control, and herbicide usage) require populations to be detected and mapped. However, the mapping is made difficult due to varying nature of the infested landscapes (e.g., uneven terrain). This paper proposes a novel method to detect and map parthenium populations in simulated pastoral environments using Red-Green-Blue (RGB) and/or hyperspectral imagery aided by artificial intelligence. Two datasets were collected in a control environment using a series of parthenium and naturally co-occurring, non-parthenium (monocot) plants. RGB images were processed with a YOLOv4 Convolutional Neural Network (CNN) implementation, achieving an overall accuracy of 95% for detection, and 86% for classification of flowering and non-flowering stages of the weed. An XGBoost classifier was used for the pixel classification of the hyperspectral dataset—achieving a classification accuracy of 99% for each parthenium weed growth stage class; all materials received a discernible colour mask. When parthenium and non-parthenium plants were artificially combined in various permutations, the pixel classification accuracy was 99% for each parthenium and non-parthenium class, again with all materials receiving an accurate and discernible colour mask. Performance metrics indicate that our proposed processing pipeline can be used in the preliminary design of parthenium weed detection strategies, and can be extended for automated processing of collected RGB and hyperspectral airborne unmanned aerial vehicle (UAV) data. The findings also demonstrate the potential for images collected in a controlled, glasshouse environment to be used in the preliminary design of invasive weed detection strategies in the field. Full article

In order to solve the problem of intelligent management of cattle numbers in the pasture, a dataset of cattle density estimation was established, and a multi-scale residual cattle density estimation network was proposed to solve the problems of uneven distribution of cattle and large scale variations caused by perspective changes in the same image. Multi-scale features are extracted by multiple parallel dilated convolutions with different dilation rates. Meanwhile, aiming at the “grid effect” caused by the use of dilated convolution, the residual structure is combined with a small dilation rate convolution to eliminate the influence of the “grid effect”. Experiments were carried out on the cattle dataset and dense population dataset, respectively. The experimental results show that the proposed multi-scale residual cattle density estimation network achieves the lowest mean absolute error (MAE) and means square error (RMSE) on the cattle dataset compared with other density estimation methods. In ShanghaiTech, a dense population dataset, the density estimation results of the multi-scale residual network are also optimal or suboptimal in MAE and RMSE. Full article

Cattle dung distribution in pastoral ecosystems is uneven and affects nutrient availability to plants. Thus, identifying its spatiotemporal patterns is crucial to understanding the mechanisms underlying the system functioning. We aimed to characterize the spatiotemporal distribution of dung patches in mixed black oat (Avena strigosa Schreb.) and Italian ryegrass (Lolium multiflorum Lam.) pastures grazed at different intensities (sward heights of 0.1, 0.2, 0.3 and 0.4 m) in the winter stocking period of an integrated soybean-beef system in southern Brazil. All dung patches were located and georeferenced every 20 days. Dung distribution was analyzed using Thiessen polygons and semivariogram analysis. The spatial pattern of dung deposition was virtually similar over time but created distinct patterns in paddocks managed at different grazing intensities. Dung patch density was greater close to attraction points, resting and socialization areas regardless of grazing intensity. Lighter grazing intensities presented stronger spatial patterns with increased dung density in those areas, but those patterns weakened with increasing grazing intensity. Dung patches covered 0.4%, 0.9%, 1.1% and 1.5% of the area in paddocks managed at 0.4, 0.3, 0.2 and 0.1 m sward heights, respectively. Geostatistics proved useful for identifying spatial patterns in integrated crop-livestock systems and will potentially support further investigations. Full article

This study describes pastoralism practiced in the Karakul village, Northeast of Tajikistan, and discusses its sustainability. Tajikistan introduced a market economy at independence in 1991, and pastoralism is now practiced on a family-unit basis. The families in Karakul graze livestock in their summer pastureland (jailoo) and move their livestock to winter pastureland around the village (kyshtoo). They make groups for pasturage with several families in jailoo and also in kyshtoo. Each group pastures their livestock every day, using a system called novad. In addition to jailoo and kyshtoo, they also practice pastoralism on two additional kinds of pastureland: küzdöö (spring pastureland) and bäärlöö (autumn pastureland). Still, now, the Karakul villagers use their pastureland as the commons: the Karakul village has not established private possession of pastureland even after a law enabled the division of common pastureland among individual families. Using the pastureland as the commons would be preferred by the local pastoralists. However, the free pasture access as the commons may result in a loss of sustainability as a trade-off. Regardless of privatization or the continued use of the commons, the possible development of the uneven use of the pastureland is inferred and should be avoided, and the introduction of a local management structure is urgently needed. Full article