Point Sampling to Stratify Biomass Variability in Sagebrush Steppe Vegetation

Cover and yield are two of the most commonly monitored plant attributes in rangeland vegetation surveys. These variables are usually highly correlated and many previous authors have suggested point-intercept estimates of plant cover could be used as a surrogate for more expensive and destructive methods of estimating plant biomass. When measurement variables are highly correlated, double sampling can be used to prestratify variability in the measurement that is more difficult or costly to obtain, thus improving sampling efficiency. The objective of this study was to examine the cost effectiveness of using point-intercept data to prestratify variability in subsequent clipped-biomass sampling on a sagebrush–bunchgrass rangeland site in southern Idaho. Point-intercept and biomass data were obtained for shrub, grass, and forb vegetation in 90 1-m² plots. These data were used to develop a synthetic population of 10 000 simulated plots for conducting sensitivity analysis on alternative double-sampling scenarios. Monte Carlo simulation techniques were used to determine the effect of sampling design on cost and variability of biomass estimates as a function of point-intercept sample size (i), number of point-intercept sample strata (s), and number of biomass samples per stratum (m). Minimization of variability in biomass estimates were always obtained from double-sampling scenarios in which a single median biomass estimate was obtained for a given stratum in the point-intercept data. Double-sampling strategies in which half of the point-intercept plots were also measured for biomass yielded a cost savings of 39% with a reduction in biomass-sample precision of 18% ± 4 SD. The relative loss of precision in biomass estimates (62% ± 12 SD) became equal to the relative cost savings of double sampling for scenarios in which the ratio of point-intercept/biomass samples exceeded a value of five.     

Estimating Aboveground Net Primary Production (ANPP) Using Landsat 8-Based Indices: A Case Study From Hir-Neur Rangelands,Iran

Aboveground Net Primary Production (ANPP), an indicator of ecosystems’ ability to capture and convert solar energy, is critical to evaluate and manage ecosystem carbon balance index including rangelands. Measuring ANPP over a large area is difficult at it varies with different factors including climate and anthropogenic, while satellite-based information shows strong opportunity. The aim of this study was to estimate the ANPP of Plant Functional Types (PFTs) using Landsat-8 imagery over the rangelands of Hir-Neur from Ardabil Province, Iran as a case study area. Landsat-8 Operational Land Imager (OLI) satellite images were collected on 9^th June 2017 aligning with the field sampling of PFTs ANPP. Eleven sampling sites with 10 plots at each site (locations recorded with a Global Positioning System, GPS) were selected along a transect with elevation gradient and the ANPP of PFTs were recorded. Twenty-two vegetation indices were calculated from the Landsat-8 imagery and the index values were extracted for the sampling plots. Correlation between derived indices and field collected ANPP data were calculated. Predictive relationships between PFTs and total ANPP and satellite indices were developed using a 3^rd-order polynomial model and the model was used to map the whole study area. The best estimation of PFTs and total ANPP was obtained using the Vegetation Index (VI3) for grasses (R²=0.47), Iron Oxide (IO) for forbs (R²=0.51), and Renormalized Difference Vegetation Index (RDVI) for shrubs (R²=0.50). The Difference Vegetation Index (DVI) was the best estimator for the total ANPP (R²=0.49). The overall accuracies of the maps were acceptable (MAE, MDE, RMSE <0.5). Results showed a difference when using PFTs for ANPP estimation in comparison with direct ANPP estimation. Thus, direct estimation of ANPP from the satellite based indices would be more accurate. This study also showed promising capabilities of the indices based on PFTs and total ANPP for estimating aboveground biomass, supply-demand balance and carbon balance at the study area and elsewhere with similar ecological conditions. This study also showed opportunity to select the most appropriate vegetation indices for the estimation of PFTs and the total ANPP.     

Grazing Abandonment Delays the Effect of Temperature on Aboveground Net Primary Production in Atlantic Grasslands

We used an Atlantic grassland system on the Iberian Peninsula to ascertain whether monthly climate variability explains variation in monthly aboveground net primary production (ANPP) and to test whether climate-ANPP relationships depend on grazing regime. In 2005, large herbivores (beef cattle, dairy sheep, and horses) were excluded through fencing three 2 500-m² plots, each located in a different location; adjacent grazed plots of equal size were established. ANPP was measured monthly during the next three growing periods (2006 ? 2008), and locally measured climate data were obtained from a public database. Because between-site variation in annual ANPP was not significant, we used data averaged across sites to test for the effect of monthly climate variability on monthly ANPP by means of dynamic regression. Enhanced ANPP was found after grazing abandonment, probably due to the sudden dominance of productive graminoids. Variation in monthly rainfall did not contribute to explain monthly ANPP under grazing or grazing exclusion. Simultaneous mean monthly air temperature explained monthly ANPP under grazing. By contrast, the effect of temperature on ANPP under grazing exclusion was delayed by 1 mo. We suggest that this delay can be explained by the development of a thick organic layer (litter) that insulated the soil in the grazing exclusion plots. However, changes in floristic composition and, consequently, in phenology might also have contributed to the differential response.     

Allometric Estimates of Aboveground Biomass Using Cover and Height Are Improved by Increasing Specificity of Plant Functional Groups in Eastern Australian Rangelands

Plant aboveground biomass (AGB) is a useful metric to assess ecosystem functioning, and its sensitivity to changing environmental conditions provides insight into potential global change impacts. Allometric estimates of AGB using vegetation characteristics such as plant cover or height provide nondestructive biomass proxies for repeated measurements but can introduce uncertainty to estimates. We estimated the relationship between both plant cover and a cover·height index and AGB for 15 plant species from six sites to identify the most reliable approach to estimate biomass nondestructively in semiarid eastern Australian rangelands. Estimates were made by grouping species at four different levels of specificity, to test whether generic estimates were more robust than grouping species based on life history and morphological characteristics. Estimates were then tested on a 1.5-m² plot at each site for validation. In all cases, models were highly significant (P < 0.001) with adjusted R² values ranging from 0.42 to 0.96 for cover models and 0.38 to 0.98 for cover·height index models. We found the addition of height improved model fits in four groups while reducing model fits in two groups. The error around AGB estimates for cover·height index−based models ranged from −66.8 to 4% (absolute mean 35%). Cover-based models had errors between −13.4% and 53% (absolute mean 14.2%). For cover-based estimates of AGB in validation plots, grouping plants by plant functional types (PFTs) increased accuracy (absolute mean error 17.3%) compared with estimates using data from all 15 species (absolute mean of 65.2%). Overall cover was a useful surrogate to estimate AGB (with the exception of one site, accuracy ranged from −2.3% to 11.5%), while height (thought to be a surrogate for canopy characteristics) provided benefit in a few circumstances. We suggest that future research should test additional nondestructive proxies and group species based on PFTs to improve AGB estimates using allometry.     

Estimating Sagebrush Biomass Using Terrestrial Laser Scanning

The presence of sagebrush (Artemisia tridentata) in rangelands has declined due to the invasion of annual grasses such as cheatgrass (Bromus tectorum) and the feedback between these flammable grasses and wildfire frequency. Monitoring the change and distribution of suitable habitat and fuel loads is an important aspect of sagebrush management, particularly under future climate conditions. Assessments of sagebrush biomass are used to monitor habitat for critical wildlife species, determine fire risk, and quantify carbon storage. Field techniques such as destructive and point-intercept sampling have been used to determine sagebrush biomass, but both of these techniques can be expensive and time consuming to implement. Light detection and ranging techniques, including airborne laser scanning and terrestrial laser scanning (TLS) have potential for rapidly assessing biomass in sagebrush steppe. This study used TLS to estimate biomass of 29 sagebrush plants in Reynolds Creek Experimental Watershed, Idaho. Biomass was estimated using TLS-derived volume, then compared with destructive samples to assess the estimation accuracy. This accuracy level was then contrasted with the estimates obtained using point-intercept sampling of the same plants. The TLS approach (R² = 0.90) was slightly better for predicting total biomass than point-intercept sampling (R² = 0.85). Prediction of green biomass, or production, was more accurate using TLS-derived volume (R² = 0.86) than point-intercept sampling (R² = 0.65). This study explores a promising new method to repeatedly monitor sagebrush biomass across extensive landscapes. Future work should focus on making this method independent of sensor type, scan distance, scan number, and study area.     

Carbon Stocks and Fluxes in Rangelands of the Río de la Plata Basin

Grasslands are one of the most modified biomes on Earth. Land use changes had a large impact on carbon (C) stocks of grasslands. Understanding the impact of land use/land cover changes on C stocks and fluxes is critical to evaluate the potential of rangeland ecosystem as C sinks. In this article we analyze C stocks and fluxes across the environmental gradients of one of the most extensive temperate rangeland areas: the Río de la Plata Grasslands (RPG) in South America. The analysis summarizes information provided by field studies, remote sensing estimates, and modeling exercises. Average estimates of aboveground net primary production (ANPP) ranged from 240 to 316 g C· m^?2·yr^?1. Estimates of belowground NPP (BNPP) were more variable than ANPP and ranged from 264 to 568 g C· m^?2·yr^?1. Total Carbon ranged from 5 004 to 15 008 g C· m^?2. Plant biomass contribution to Total Carbon averaged 13% and varied from 9.5% to 27% among sites. The largest plant C stock corresponded to belowground biomass. Aboveground green biomass represented less than 7% of the plant C. Soil organic carbon (SOC) was concentrated in the slow and passive compartments of the organic matter. Active soil pool represented only 6.7% of the SOC. The understanding of C dynamics and stocks in the RPG grasslands is still partial and incomplete. Field estimates of ANPP and BNPP are scarce, and they are not based on a common measurement protocol. Remotely sensed techniques have the potential to generate a coherent and spatially explicit database on ANPP. However, more work is needed to improve estimates of the spatial and temporal variability of radiation use efficiency. The absence of a flux tower network restricts the ability to track seasonal changes in C uptake and to understand fine-scale controls of C dynamics.     

Influence of 90 Years of Protection From Grazing on Plant and Soil Processes in the Subalpine of the Wasatch Plateau,USA

Human communities in the Intermountain West depend heavily on subalpine rangelands because of their importance in providing water for irrigation and forage for wildlife and livestock. In addition, many constituencies are looking to managed ecosystems to sequester carbon in plant biomass and soil C to reduce the impact of anthropogenic CO₂ on climate. This work builds on a 90-year-old grazing experiment in mountain meadows on the Wasatch Plateau in central Utah. The purpose of this study was to evaluate the influence of 90 years of protection from grazing on processes controlling the input, output, and storage of C in subalpine rangelands. Long-term grazing significantly reduced maximum biomass in all years compared with plots within grazing exclosures. For grazed plots, interannual variability in aboveground biomass was correlated with July precipitation and temperature (R² = 0.51), while there was a weak correlation between July precipitation and biomass in ungrazed plots (R² = 0.24). Livestock grazing had no statistically significant impacts on total soil C or particulate organic matter (POM), although grazing did increase active soil C and decrease soil moisture. Grazing significantly increased the proportion of total soil C pools that were potentially mineralizable in the laboratory, with soils from grazed plots evolving 4.6% of total soil C in 1 year while ungrazed plots lost 3.3% of total soil C. Volumetric soil moisture was consistently higher in ungrazed plots than grazed plots. The changes in soil C chemistry may have implications for how these ecosystems will respond to forecast climate change. Because grazing has resulted in an accumulation of easily decomposable organic material, if temperatures warm and summer precipitation increases as is anticipated, these soils may become net sources of CO₂ to the atmosphere creating a positive feedback between climate change and atmospheric CO₂.     

Primary Productivity and Precipitation-Use Efficiency in Mixed-Grass Prairie: A Comparison of Northern and Southern US Sites

Precipitation-use efficiency (PUE) is a key determinant of aboveground net primary production (ANPP). We used long-term datasets to contrast ANPP and PUE estimates between northern (southeast Montana) and southern (north Texas) mixed-grass prairies. Effects of varying amounts and temporal distribution of precipitation on PUE were examined at the Montana site, using a rainout shelter and irrigation. Results show that 1) ANPP was 21% less in Montana than Texas (188 g · m^-2 vs. 237 g · m^-2); 2) plant function type (PFT) composition varied between the two study locations, with cool-season perennial grasses (CSPG) dominating in Montana (52%) and warm-season perennial grasses (WSPG) dominating in Texas (47%); 3) production dynamics varied between the two sites with 90% of ANPP completed by 1 July in Montana as compared to 31 August in Texas; 4) average PUE estimates were greater in Montana (0.56 g dry matter · m^-2 · mm^-1 of precipitation) than Texas (0.40 g · m^-2 · mm^-1); and 5) contributions to PUE estimates varied among PFT and location, with CSPG estimates being greater in Montana than Texas (52% vs. 31%) and WSPG estimates being greater in Texas than Montana (47% vs. 27%). Seasonal droughts and supplemental irrigations at the Montana site substantially altered ANPP, PFT biomass composition, and PUE. Results show PUE was responsive to PFT composition relative to amount and seasonal distribution of precipitation. Therefore, one should expect changes in ANPP and PUE to occur with shifts in precipitation patterns until PFT composition becomes adjusted to the regime.     

Large-Scale Spatial Patterns of Grassland Community Properties in the Inner Mongolia Autonomous Region,China

Mapping large-scale spatial patterns of grassland community properties in the Inner Mongolia Autonomous Region of China and learning how they are affected by environmental factors are vital to understand grassland changes in response to climate change and human activity. We collected data on six grassland community properties across 198 sample plots in the Inner Mongolia Autonomous Region: height, coverage, aboveground biomass (AGB), belowground biomass (BGB), soil bulk density (SBD), and species number (SN). We then analyzed the relationship between these and a range of environmental factors, including elevation, mean annual temperature (MAT), mean annual precipitation (MAP), ≥ 10 C annual accumulated temperature, humidity index, and normalized difference vegetation index (NDVI), using correlation and regression analysis. On the basis of the regression equation, we undertook a multifactor model using ArcGIS, in which different weights were assigned to each factor according to the degree of fitness between the estimated results and measured data. We then mapped the spatial distribution of grassland community properties in Inner Mongolia. We found a significant correlation between all of the grassland community properties and environmental factors measured (P < 0.01). In terms of spatial patterns, SN, height, coverage, AGB, and BGB were positively correlated with the transition from desert grassland to meadow grassland. The community properties model provided good results, with average accuracies of 53.05–90.21% and R² values of 0.40–0.68 (P < 0.01) across the six grassland community properties. The multifactor comprehensive model provides significant correlation between the predicted results and measured data. Therefore, this could be used as a basis for future studies on Inner Mongolia grasslands and to understand temporal and spatial changes of grassland in response to human activity and climate change.     

Number of Samples Required for Estimating Herbaceous Biomass

Although the precision of herbaceous biomass estimation depends on the sample number, the spatial heterogeneity of the biomass, and sampling procedures, the magnitudes of the influences on the precision have not been clarified. We simulated virtual plant communities based on the gamma distribution to clarify the relationships between the precision of estimating herbaceous biomass and the number of samples, sampling density, spatial heterogeneity of the biomass, and sampling procedures. Using only two parameters, the gamma distribution can approximate the frequency distribution of herbage mass with varying heterogeneity. Our simulations demonstrated that the number of samples is a more influential factor than sampling density on the precision of the herbaceous biomass estimation. Moreover, our simulations confirmed that biomass heterogeneity strongly affected the precision and quantified the magnitude of the influence. When we estimated biomass with random sampling and a 50 × 50 cm quadrat and accepted estimation error of ± 10% of the mean for a confidence interval of 95%, the numbers of samples needed were 200, 77, and 9 for very, intermediate, and less heterogeneous grasslands, respectively. Similarly, when we estimated biomass with a ranked set sampling (RSS), then 24, 15, and 4 samples were needed in very, intermediate and less heterogeneous grasslands, respectively. We came to two conclusions: 1) In less heterogeneous grasslands, good precision of estimation can be obtained with a small number of samples, and it is useful to employ RSS. The cutting method, as well as nondestructive methods, will be practical; and 2) estimation for heterogeneous grassland requires a large number of samples, and it is not so useful to employ RSS. For that reason, more research is needed on nondestructive methods.     

Effects of 34 Years of Experimentally Manipulated Burn Seasons and Frequencies on Prairie Plant Composition

Historically, tallgrass prairie burns occurred at many seasons and frequencies. Currently, tallgrass prescribed burns often occur annually in the spring, usually for cattle forage production. Altering burning season and frequency is known to affect plant composition and biomass production, but researchers are still uncertain how burning season and frequency interact. We present the long-term effects of a factorial combination of different burn seasons (spring, summer, autumn, or variable [rotated through seasons]) and frequencies (annual or quadrennial) on the plant composition and biomass production of an ungrazed, restored tallgrass prairie in eastern Nebraska, United States. The experimental plots were established in 1978 and visually surveyed for baseline data in 1979 and 1981. Experimental burn treatments were begun in 1982. Plots were visually surveyed until 2011 with the following results: 1) annual spring and summer burns increased C₄ graminoid abundance; 2) annual autumn burns increased forb abundance; 3) burn season had little effect on plant composition for quadrennial burns; and 4) variable season burns generally led to plant composition that was intermediate between annual spring/summer and annual autumn burns. We also clipped biomass to estimate aboveground annual net primary production (ANPP) in 2015, a year in which both annual and quadrennial burns occurred. Total ANPP did not differ significantly between burn frequencies nor between spring and autumn burns (772 g m^? 2 average) but was lower in summer burns (541 g m^? 2). ANPP results were similar to visual surveys, with significantly higher C₄ graminoid ANPP in spring than autumn burns and significantly lower forb and C₃ graminoid ANPP in spring than autumn burns. Overall, these results suggest autumn burns can increase forb and C₃ graminoid abundance, without strongly affecting total ANPP relative to spring burns. Future studies should compare plant and livestock production between spring and autumn burns in grazed fields.     

Do Introduced Grasses Improve Forage Production on the Northern Mixed Prairie

Relative benefit of introducing forage species to the Northern Great Plains have been examined with contradictory conclusions. In most cases, studies were either confounded by time of establishment or treatments were not randomized and lacked independence. We examined aboveground net primary production (ANPP) in northern mixed prairie using a randomized complete block design with four treatments: crested wheatgrass (Agropyron cristatum [L.] Gaertn.), Russian wildrye (Psathyrostachys juncea [Fisch.] Nevski), a native control that was not harvested, and a harvested native. The experiment was conducted in a Stipa–Agropyron–Bouteloua site and a Stipa–Bouteloua site over 13 yr and 12 yr, respectively. The data were analyzed by sampling period (Stipa–Agropyron–Bouteloua: 1, 1994 to 1997; 2, 1998 to 2001; 3, 2002 to 2006; and Stipa–Bouteloua: 1, 1995 to 1998; 2, 1999 to 2002; 3, 2003 to 2006). ANPP among treatments was influenced (P < 0.05) by site and its interaction with treatment and sampling period (1 to 3). ANPP from the native-control, harvested-native, crested wheatgrass, and Russian wildrye treatments was 220.9, 183.9, 300.8, and 189.6 g · m^–2 (SEM = 11.2), respectively, in the Stipa–Agropyron–Bouteloua site and 122.9, 98.2, 216.3, and 115.9 g · m^–2 (SEM = 12.0), respectively, in the Stipa–Bouteloua site. Mean ANPP (SEM) within each sampling period (1 to 3) was 186.4 (9.1), 135.4 (5.8), and 263.9 (8.8) g · m^–2 in the Stipa–Agropyron–Bouteloua site, respectively, and 124.5 (6.4), 138.6 (6.1), and 151.3 (10.5) g · m^–2 in the Stipa–Bouteloua site, respectively. Russian wildrye in the Stipa–Bouteloua site and crested wheatgrass in both sites was relatively more productive in the first period after establishment than in subsequent years. The study confirms the relative ANPP advantage of crested wheatgrass over native on the Stipa–Bouteloua site but not on the Stipa–Agropyron–Bouteloua site, whereas Russian wildrye exhibited no ANPP advantage over the native on either site.     

Spatial and Temporal Variability in Aboveground Net Primary Production of Uruguayan Grasslands

Aboveground net primary production (ANPP) is a variable that integrates many aspects of ecosystem functioning. Variability in ANPP is a key control for carbon input and accumulation in grasslands systems. In this study, we analyzed the spatial and temporal variability of ANPP of Uruguayan grasslands during 2000–2010. We used enhanced vegetation index (EVI) data provided by the MODIS-Terra sensor to estimate ANPP according to Monteith's (1972) model as the product of total incident photosynthetically active radiation, the fraction of the radiation absorbed by green vegetation, and the radiation use efficiency. Results showed that ANPP varied spatially among geomorphological units, increasing from the north and midwest of Uruguay to the east and southeast. Hence, Cuesta Basáltica grasslands were the least productive (399 g DM · m^-2 · yr^-1), while grasslands of the Sierras del Este and Colinas y Lomas del Este displayed the highest productivity (463 and 465 g DM · m^-2 · yr^-1, respectively). This pattern is likely related to differences in soil depth and associated variation in water availability among geomorphological units. Seasonal variability in ANPP indicated peak productivity in the spring in all units, but differences in annual trends over the 10-yr study period suggested that ANPP drivers are operating spatially distinct. Understanding the spatial and temporal variability of ANPP of grasslands are prerequisites for sustainable management of grazing systems.     

Feed Intake and Performance of Sheep Grazing Semiarid Grassland in Response to Different Grazing Systems

Effects of grazing management systems (GS) on biomass production and nutritional quality of rangeland vegetation in semiarid regions are extensively studied; however, limited information is available regarding their effects on diet digestibility and feed intake of grazing livestock. We therefore analyzed digestibility of ingested organic matter (dOM), organic matter intake (OMI), and live weight gain (LWG) of sheep in a grazing experiment established in the Inner Mongolian steppe of China, where two GS were tested for six different grazing intensities (GI) from very light to heavy grazing. For the continuous grazing system, sheep grazed the same plots each year, and for the alternating system, grazing and hay making were alternated annually between two adjacent plots. In July, August, and September 2009 and 2010, feed intake and live weight of sheep were determined. The GS did not affect dOM (P = 0.101), OMI (P = 0.381), and LWG of sheep (P = 0.701). Across both GS LWG decreased from 98 g · d^-1 for GI1 to 62 g · d^-1 for GI6 (P &spilt; 0.001; R² = 0.42). There were no interactions between GS and GI for all measured parameters (P ≥ 0.061), indicating that alternating grazing did not compensate for negative effects of heavy grazing even after 4 yr of grassland use. In summary, our study showed that irrespective of GI, alternating grassland use does not improve dOM, OMI, and hence, LWG of sheep. However, it might enhance revenues and ecological sustainability in the long term when compared to the common practice of continuous grazing at very high stocking rates.     

Effect of Phosphate Fertilization on Flooding Pampa Grasslands (Argentina)

We postulate that phosphorus (P) fertilization may increase above-ground net primary productivity (ANPP) of rotationally grazed rangelands without reducing the legume component, as does N fertilization. In doing so, we evaluated the effect of phosphate fertilization on the production and relative contribution of legumes and grasses of native and old tall fescue (Festuca arundinacea Schreb) grasslands; we recorded annual production, seasonal productivity, and biomass contribution of each component. The experiment was conducted in a commercial farm located in the Flooding Pampa and managed under rotational grazing. Treatments consisted of two fertilization programs (66 (P₆₆) and 29 (P₂₉) kg P · ha^-1 supplied as rock phosphate and/or monoammonium phosphate from 1997 to 1999) and a nonfertilized control. A paddock dominated by native grassland and another dominated by old tall fescue grassland were selected. Nine 5-ha plots were established in each paddock, and treatments were randomly assigned. During the experimental period, from October 1998 to October 1999, total above-ground biomass was harvested from each plot before and after each grazing period and separated into components: tall fescue, other C₃ perennial grasses, legumes, C₃ annual grasses, C₄ grasses, forbs, and standing dead material. ANPP of each component was estimated during the warm (October 1998–February 1999) and the cool (March 1999–September 1999) season. In native grassland, phosphate fertilization increased ANPP of C₃ annual grasses and legumes during both the warm and the cool seasons; therefore annual ANPP of the grassland under P₆₆ was 40% higher than under P₂₉ and doubled ANPP of nonfertilized plots. Phosphate fertilization didn't increase total annual ANPP of old tall fescue grassland, but it did increase ANPP of legumes during both seasons.     

Quantitative Estimation of Biomass of Alpine Grasslands Using Hyperspectral Remote Sensing

In order to promote the application of hyperspectral remote sensing in the quantification of grassland areas’ physiological and biochemical parameters, based on the spectral characteristics of ground measurements, the dry AGB and multisensor satellite remote sensing data, including such methods as correlation analysis, scaling up, and regression analysis, were used to establish a multiscale remote sensing inversion model for the alpine grassland biomass. The feasibility and effectiveness of the model were verified by the remote sensing estimation of a time-space sequence biomass of a plateau grassland in northern Tibet. The results showed that, in the ground spectral characteristic parameters of the grassland’s biomass, the original wave bands of 550, 680, 860, and 900 nm, as well as their combination form, had a good correlation with biomass. Also, the remote sensing biomass estimation model established on the basis of the two spectral characteristics (VI2 and Normalized Difference Vegetation Index [NDVI]) had a high inversion accuracy and was easy to realize, with a fitting R² of 0.869 and an F test value of 92.6. The biomass remote sensing estimate after scale transformation had a standard deviation of 53.9 kg/ha from the fitting model established by MODIS NDVI, and the estimation accuracy was 89%. Therefore, it displayed the ability to realize the estimation of large-scale and long-time sequence remote sensing biomass. The verification of the model’s accuracy, comparison of the existing research results of predecessors, and analysis of the regional development background demonstrated the effectiveness and feasibility of this method.     

Diagnostic Feed Values of Natural Grasslands Based on Multispectral Images Acquired by Small Unmanned Aerial Vehicle

Grasslands are the largest renewable source of terrestrial chlorophytes. Furthermore, grasslands can be both fiber sources and the primary metabolizable energy source for ruminants. Therefore, rapid, accurate, and large-scale monitoring of grassland ecosystems is important to provide spatial information on forage quality control and rangeland management. In this experiment, 100 grassland sites were randomly selected in two study areas. A multiaxis unmanned aerial vehicle (UAV) made 26 flights over those areas to capture spectral images during August 2016, which enabled the acquisition of vegetation index values of the grassland sites. Next, grassland plots were harvested and the nutritional composition of the grass was determined. After selecting the most sensitive spectral information for each nutritional value, retrieval models for grassland nutrition were constructed. Predictor variables of the models were then tested on the samples. The results demonstrate that there are correlations between nutritional values and vegetation indices. The predicted values of the coefficient of determination (R²-P) and root mean square error (RMSE) for dry matter (DM) were 0.676% and 4.719%. The same values for crude protein (CP) were 0.653% and 1.361%. The R²-P and RMSE values for in vitro DM digestibility (IVDMD) prediction models were weak, but they could be improved by more sensitive wavelengths and improved mathematical models to fit the data. The results show that UAV remote sensing can be used to estimate the feed values of natural grassland and that this sensing approach provides a rapid, flexible, and efficient method of estimating feed values. Although the prediction models for nutritional values need to be improved, they still opened perspectives for the use of UAV-based remote sensing in rangeland management and grassland husbandry.     

GPS Collar Sampling Frequency: Effects on Measures of Resource Use

A challenge in animal behavior studies using Global Positioning System (GPS) collars is selecting a sampling frequency to accomplish desired goals. High data resolution (i.e., frequent sampling) is appealing, because it maximizes behavioral information garnered. Extended sampling might be needed, however, to describe long term behaviors or seasonal dynamics. Because tradeoffs exist between high data resolution and sampling duration, we evaluated the effects of variable GPS sampling intervals on proportions of pastures used by cattle and distance traveled per day. This was accomplished with GPS collars configured to record cattle positions every 5 min for 15 d in three 829–864-ha pastures. Data were iteratively reduced to simulate increasingly longer GPS recording intervals from once every 10 min up to once daily. Two techniques were used to measure the percentage of pastures accessed by cattle. The first counted only pixels containing GPS coordinates. The second counted pixels containing coordinates and/or traversed by lines between vertices. Expansion of GPS recording intervals decreased (P < 0.01) estimates of the proportion of pastures visited by cattle with rates of decline best fit by exponential decay functions for both line and point techniques (R² = 0.93 and 0.97, respectively). Spatial errors accompanying less frequent sampling intervals, however, were extremely large with the line technique and misrepresented areas visited by cattle. Expansion of GPS sampling intervals decreased (P < 0.001) distance traveled per day by cattle about 10% with each iteration. If travel corridors or accurate assessments of resources accessed are of critical concern, then longer GPS integration intervals should be avoided because they propagate flawed spatial interpretations. Similarly, if accurate measures of travel distances are critical, we suggest using a relatively frequent GPS recording interval.     

Estimates of Willow (Salix Spp.) Canopy Volume using Unmanned Aerial Systems

Management of livestock grazing in riparian areas is an important aspect of rangeland management. Willows (Salix spp.) are a common riparian plant serving as an ecosystem stabilizer, as well as providing important habitat, but browsing or trampling by cattle can decrease willow canopy volume. Canopy volume can be measured on the ground with hours of meticulous data collection. However, canopy volume estimates from drone-collected images could be a more efficient and objective method for measuring willow canopy volume and understanding the impact of livestock use on riparian woody vegetation. Our objective was to determine how well drone-based measurements of willow canopy volume corresponded to field measurements in a southern Idaho riparian area before and after a grazing trial. We used sets of overlapping aerial images from a DJI Phantom 4 Professional drone to construct 3-dimensional point clouds of willows. From these point clouds we estimated willow canopy volume using 2 techniques and compared those with canopy volume estimates from field measurements of 58 willows ranging in height from 0.76 m to 4.57 m. Point cloud canopy volume estimates using both techniques showed high correspondence with field-estimated volume (R² > 0.8) for both pregrazing and postgrazing. However, point cloud techniques generally underestimated canopy volume compared with the field technique. Drone-based estimates took ≈4 h per sampling event (i.e., pregrazing, postgrazing) including acquiring and processing the imagery, whereas field-based measurements took ≈10 h per sampling event. These results demonstrate drone-collected images may be an effective tool for measuring and monitoring riparian woody vegetation.     

Above-Ground Net Primary Production for Elymus lanceolatus and Hesperostipa curtiseta After a Single Defoliation Event

Above-ground net primary production (ANPP) of northern wheatgrass (Elymus lanceolatus [Scribn. & J. G. Sm.] Gould) and western porcupine grass (Hesperostipa curtiseta [Hitchc.] Barkworth) was determined after defoliation to a 7.5 cm stubble height on five landform elements in the Northern Mixed Prairie that had been ungrazed for > 25 yr. Landform elements included north aspect–concave slopes, north aspect–convex slopes, south aspect–concave slopes, south aspect–convex slopes, and level uplands. ANPP was determined for 2 yr after defoliating plots once in May, June, July, August, September, October, November, or April. Northern wheatgrass and western porcupine grass ANPP varied among landform elements (P < 0.01), but not with the month of defoliation × landform element interaction (P ≥ 0.22). Month of defoliation did not influence ANPP of northern wheatgrass (P ≥ 0.69), but that of western porcupine grass was reduced by August and September defoliations (P < 0.01). ANPP of both grasses was insensitive to landform element in terms of defoliation responses. Northern wheatgrass ANPP was not responsive to temporal aspects of a single defoliation. With the exception of August and September defoliations, western porcupine grass also was insensitive to a single defoliation in different months. Land managers should consider rest (1 yr nongrazing) following grazing of western porcupine grass in August or September, whereas responses to defoliation in different months suggest northern wheatgrass can be grazed annually.