Grasses

Numerous studies indicate that the Tibet Autonomous Region’s grasslands have experienced widespread greening since remote sensing data became available. While climate warming and moistening can drive this trend, there is growing interest in quantifying the effect of non-climatic factors, including human activities. A widely used method estimates these effects by comparing potential and actual vegetation productivity. This study focuses on Ngari, a region constrained by both temperature and moisture. We constructed a multiple regression model using climate variables to predict NDVI and to achieve a good fit for as many pixels as possible. Residual trends, analyzed via the Kendall Tau method, reflect vegetation dynamics after removing climatic effects—a form of statistical control. Results show that grassland NDVI in Ngari increased overall (2000–2024), with 73% of pixels showing a positive Kendall Tau (among them 34% were significant at p < 0.05). The best-performing model used July–August SPEI, April–July precipitation, and mean temperature. After removing climate effects, pixels with a positive Kendall Tau rose to 74.1% (among them 21% were significant at p < 0.05), indicating that non-climatic factors exerted a net positive influence on Ngari’s grassland trends from 2000 to 2024.

This study aimed to assess the potential of machine learning models applied to high spatial resolution images from UAVs for estimating the aboveground biomass (AGB) of forage grass cultivated in the Brazilian semiarid region. The fresh and dry AGB were determined in Cenchrus ciliare plots with an area of 0.04 m². Spectral data were obtained using a multispectral sensor (Red, Green, and NIR) mounted on a UAV, from which 45 vegetation indices were derived, in addition to a structural variable representing plant height (H95). Among these, H95, GDVI, GSAVI2, GSAVI, GOSAVI, GRDVI, and CTVI exhibited the strongest correlations with biomass. Following multicollinearity analysis, eight variables (R, G, NIR, H95, CVI, MCARI, RGR, and Norm G) were selected to train Random Forest (RF), Support Vector Machine (SVM), and XGBoost models. RF and XGBoost yielded the highest predictive performance, both achieving an R² of 0.80 for AGB—Fresh. Their superiority was maintained for AGB—Dry estimation, with R² values of 0.69 for XGBoost and 0.67 for RF. Although SVM produced higher estimation errors, it showed a satisfactory ability to capture variability, including extreme values. In modeling, the incorporation of plant height, combined with spectral data obtained from high spatial resolution imagery, makes AGB estimation models more reliable. The findings highlight the feasibility of integrating UAV-based remote sensing and machine learning algorithms for non-destructive biomass estimation in forage systems, with promising applications in pasture monitoring and agricultural land management in semi-arid environments.

Achieving satisfactory levels of weight gain for developing replacement beef heifers is challenging when utilizing toxic endophyte-infected tall fescue (Schedonorus arundinaceus) as the primary forage. This is due to the intensifying impact of ergot alkaloids produced by the fungal endophyte on heifer heat stress in the summer. The purpose of this trial was to determine if clipping hair coats would reduce heat stress impacts experienced by fall-born heifers stocked on toxic endophyte-infected tall fescue. Heifers were randomly assigned to a control cohort and a clipped cohort. The heifers in the clipped treatment group were sheared along the body of the heifer. Vaginal temperature loggers were used to record core temperatures every ten minutes during several sampling periods. Hair coats on clipped heifers resembled hair coats of the control heifers by the conclusion of the 16-week trial. Average daily gains of the clipped heifers exceeded the average daily gains of the control heifers only in the first four-week period. There were no differences in seasonal average daily gain or pregnancy rates. Clipped heifers had cooler core temperatures by 0.2–0.3 °C in the morning compared to the control heifers. Clipping hair coats of heifers only provided short-term relief for cattle stocked on toxic endophyte-infected tall fescue.