Rangeland Biomass Research Articles

Estimation of standing crop biomass in rangelands of the Middle Atlas mountains using remote sensing data

In the Middle Atlas rangelands, traditional methods for estimating standing crop biomass are labour-intensive and impractical. Remote sensing offers an initiative for standing crop biomass large-scale monitoring. The aim of this study was to estimate standing crop biomass, comprising annual and perennial forbs, grasses and perennial shrubs, using remote sensing data. The vegetation indices (NDVI, DVI, RVI, MSAVI and OSAVI) were derived from medium-resolution Landsat 8 and MODIS imagery. Sixty sampling sites were used for the biomass data collection. These sites were located across three grazing areas and data were collected in May and June 2016. Regression models were established between biomass field data and the five indices. Correlation analysis indicated that among the five vegetation indices, only DVI had the lowest value (r = 0.60). Linear models developed between the biomass field data and vegetation indices showed that NDVI, OSAVI and RVI explained a reasonable percentage of the variance in biomass. Values for R 2 were 0.74, 0.77 and 0.71, respectively. Among these indices, the OSAVI performed better, with a high R 2 and low error (MAPE = 11.03%). The established models represent a key tool for long-term monitoring of these rangelands.

Tracking Livestock Diets Using Stable Isotopes in Complex Mixed Farming Systems

Stable isotope analysis of 13C and 15N has been widely used to provide information regarding short- or long-term animal dietary composition as affected by changes in land or ecological system use. In complex mixed-crop−livestock systems, rangeland biomass, crop residues, and feedstuff contribute to livestock diets with high seasonal variations. Particularly in the Limpopo province, South Africa, livestock feed supply varies considerably throughout the year leading to regular feed gaps. Hence, to estimate livestock diet composition in relation to these variations, we analyzed the stable isotopes of C and N of tail hair and feces samples across free-ranging beef cattle in distinct agroecological zones (AEZs) and farm types. Here, farm types were structured according to mixed-crop−livestock farms and livestock-only farms. We found that farm types did not influence the δ13C of feces and hair (P > 0.05), but AEZ had a strong effect on δ13C patterns (P < 0.01). The contribution of C3-C4 plants in the diet varied considerably across farm types and AEZ (P < 0.01). The δ15N values indicated nutritional stress, probably due to low protein concentrations in feed (e.g., rangeland biomass, crop residues) irrespective of AEZ and farm types. In general, cattle exposed to arid conditions (drier AEZ) had significantly decreased δ13C values and increased δ15N values that could potentially be linked to feed gaps. Moreover, the results from the present study indicate that δ13C of feces and hair samples are more meaningful predictors of cattle diet than δ15N as affected by different farm practices. Therefore, stable isotope analyses potentially have an important role in helping improve the understanding of seasonal shifts in livestock diet intake in diverse production systems.

Drought‐tolerant succulent plants as an alternative crop under future global warming scenarios in sub‐Saharan Africa

AbstractGlobally, we are facing an emerging climate crisis, with impacts to be notably felt in semiarid regions across the world. Cultivation of drought‐adapted succulent plants has been suggested as a nature‐based solution that could: (i) reduce land degradation, (ii) increase agricultural diversification and provide both economic and environmentally sustainable income through derived bioproducts and bioenergy, (iii) help mitigate atmospheric CO2 emissions and (iv) increase soil sequestration of CO2. Identifying where succulents can grow and thrive is an important prerequisite for the advent of a sustainable alternative ‘bioeconomy’. Here, we first explore the viability of succulent cultivation in Africa under future climate projections to 2100 using species distribution modelling to identify climatic parameters of greatest importance and regions of environmental suitability. Minimum temperatures and temperature variability are shown to be key controls in defining the theoretical distribution of three succulent species explored, and under both current and future SSP5 8.5 projections, the conditions required for the growth of at least one of the species are met in most parts of sub‐Saharan Africa. These results are supplemented with an analysis of potentially available land for alternative succulent crop cultivation. In total, up to 1.5 billion ha could be considered ecophysiologically suitable and available for succulent cultivation due to projected declines in rangeland biomass and yields of traditional crops. These findings may serve to highlight new opportunities for farmers, governments and key stakeholders in the agriculture and energy sectors to invest in sustainable bioeconomic alternatives that deliver on environmental, social and economic goals.

Remotely sensed normalized difference red-edge index for rangeland biomass estimation

PurposeThe purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years. The connection between vegetation index and field biomass will be used to assign probabilities, but in some cases, it does not provide acceptable results because of soil background and geographical and temporal variability.Design/methodology/approachIn this study, the normalized difference red-edge (NDRE) index was used to calculate the rangeland biomass in comparison to five vegetation indices. Field measurements of biomass of natural rangeland in the West of Iran were taken in 2015, 2018 and 2021, and SENTINEL-2 data were used for analysis.FindingsThe results indicated that the overall advantage of NDRE stems from the fact that it adjusts for changes in leaf water content while overcoming the detrimental effects of soil substrate heterogeneity, both of these factors have a significant impact on pasture biomass. These results suggest that an NDRE-based biomass estimation model might be useful for estimating and monitoring biomass in large rangelands with significant geographical and temporal variability.Originality/valueIdentifying the best vegetation index to establish a vegetation-based biomass regression model for rangelands in large areas with different climatic conditions, plant compositions and soil types is the overall aim of this study.

Borana rangeland of southern Ethiopia: Estimating biomass production and carrying capacity using field and remote sensing data

Assessing rangeland productivity is critical to reduce ecological degradation and promote sustainable livestock management. Here, we estimated biomass productivity and carrying capacity dynamics in the Borana rangeland of southern Ethiopia by using field-based data and remote sensing data (i.e., normalized difference vegetation index (NDVI)). Data was collected from both rainy and dry seasons when biomass production was high and low respectively. Results of linear regression showed that both biomass production (R2adj = 0.672) and NDVI value (R2adj = 0.471) were significantly decreased from 1990 to 2019. Field data and NDVI values for mean annual biomass showed a significant linear relationship. The model accuracy in the annual relationship between the observed and predicted biomass values was strong (R2adj = 0.986) but with high standard error, indicating that the observed biomass production in the rangeland area was not in good condition as compared with the predicted one. This study suggests that, using NDVI data and field-based data in combined way has high potential to estimate rangeland biomass and carrying capacity dynamics at extensively grazed arid and semi-arid rangelands. And to use for estimating stoking rates and predicting future management techniques for decision making.

Estimation of Rangeland Production in the Arid Oriental Region (Morocco) Combining Remote Sensing Vegetation and Rainfall Indices: Challenges and Lessons Learned

This study aimed at investigating the potential of vegetation indices and precipitation-related variables derived from remote sensing to assess rangeland production in the arid environment of the Moroccan Oriental region and identifying the challenges linked to that particular biome. Vegetation indices (VIs) and the Standardized Precipitation Index (SPI) computed at various aggregation periods were first integrated into a Random Forest model. In a second step, we studied in more detail the linear relationship between rangeland biomass and one of the spectral indices (ARVI) for the various vegetation formations present in the area. We concluded that, mostly due to the presence of alfa steppes (Stipa tenacissima), and especially to a large proportion of non-photosynthetic vegetation, it is not possible to accurately estimate rangeland production with a global model in this region. We recommend separating Stipa tenacissima from the other species in models and focusing on methods aimed at studying dry and non-photosynthetic vegetation to improve the quality of the prediction for alfa steppes.

Modeling Integrated Impacts of Climate Change and Grazing on Mongolia’s Rangelands

Mongolia contains some of the largest intact grasslands in the world, but is vulnerable to future changes in climate and continued increases in the number of domestic livestock. As these are two major drivers of change, it is important to understand interactions between the impact of climate and grazing on productivity of Mongolia’s rangelands and the livelihoods they sustain. We use a gridded, spatially explicit model, the Rangeland Production Model (RPM), to explore the simultaneous and interacting effects of climate and management changes on Mongolia’s rangeland and future livestock production. Comparing the relative impact of temperature, precipitation, and grazing intensity, varied individually and in combination, we find that climatic factors dominate impacts on forage biomass and animal diet sufficiency. Site rainfall strongly mediates the impact of grazing on standing biomass, such that more productive or higher-rainfall sites are more vulnerable to increases in grazing pressure. Gridded simulations covering Mongolia’s Gobi-Steppe ecoregion show that while rangeland biomass is generally predicted to increase under future climate conditions, interactions among spatially varying drivers create strong heterogeneity in the magnitude of change.

It depends on the rain: Smallholder farmers’ perceptions on the seasonality of feed gaps and how it affects livestock in semi-arid and arid regions in Southern Africa

The risk of climate-induced feed gaps, i.e. seasonal deficiencies in forage quantity and quality, is a major constraint for livestock in the dry regions of southern Africa. In South Africa particularly, the frequent occurrence of drought is a challenge for livestock farming and, coping strategies to mitigate feed gaps on smallholder farms are urgently needed. We chose the Limpopo province, of northern South Africa to study livestock farmers’ perceptions of the temporal patterns of feed gaps and their perceived impacts on livestock production across different agro-ecological zones (AEZ) and farm types (i.e., livestock only, mixed crop-livestock farms). We combined a semi-structured questionnaire on ninety farms with data from herbage analysis (mineral nutrient concentrations of grasses grazed in winter). Additionally, we explored the effect of seasonal feed availability on feed gaps, expressed as gross primary productivity (GPP), based on long-term simulated vegetation data. We found a close correlation between farmers’ perceived feed gaps and GPP (Pearson’s r = − 0.77, p < 0.01). Farmers’ perceptions of feed gaps are related to precipitation deficits that restrict rangeland productivity especially in winter and spring across the AEZ. Consequently, farmers considered that feed gaps occur mainly in winter (80%) followed by spring (30%) and autumn (20%). In addition, our analysis demonstrated that in winter the mineral concentration in rangeland biomass is inadequate to meet the livestock feed requirements. The percentages of farmers who perceived feed gaps and animal weight loss in the winter season did not differ significantly between farm types (p = 0.40) and AEZ (p = 0.41). Among livestock-only farmers, feed gaps were perceived to occur more in autumn (p < 0.01) whereas for mixed crop-livestock farmers the feed gap perception was greater in spring (p < 0.01). Farmers located in the drier zone perceived feed gaps more in spring (p < 0.05), leading to the significant perception of livestock weight loss for that period (p < 0.01). As strategies to deal with feed gaps, farmers rely on crop residues and/or reduction of livestock numbers. To improve the sustainability of the livestock system, our results show that feed gaps follow a strong seasonal pattern and they suggest that intervention strategies do not necessarily need to account for local climatic differences but rather for farm operation types.

Prediction of grass biomass from satellite imagery in Somali regional state, eastern Ethiopia

The drought-prone Ethiopian Somali region has a long history of pastoralism (livestock grazing), which is a major source of livelihoods. However, it suffers from poor rangeland management and a lack of research and information. The objectives of this study were to develop a method for forecasting forage biomass and to quantify production of and spatial variation in forage from satellite imagery. We downloaded Sentinel-2 images and processed spectral information in the blue, red, and near-infrared bands, and calculated the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Combining ground sampling (on 55 plots) with remote sensing data, we developed a forage forecasting model for the area. Forage (biomass) was significantly correlated with both EVI (R2 = 0.87; P < 0.001) and NDVI (R2 = 0.81; P < 0.001). Both gave good predictions of forage biomass in the district. We estimated the annual biomass in each grassland pixel at the peak of the growing season. Models based on each index revealed close estimates: NDVI indicated an average of 0.76 t/ha and a total of 38 772 t/year; EVI indicated an average of 0.78 t/ha and a total of 39 792 t/year. The estimated rangeland biomass showed high spatial variability of 0.22–4.89 t/ha.year. For future rangeland management in the area, the proposed approach and models can be used to estimate available forage biomass from satellite imagery in the middle of the grass growing season (2 months after the rains start), before the grass matures and is harvested.

Do soil-adjusted or standard vegetation indices better predict above ground biomass of semi-arid, saline rangelands in North-East Iran?

ABSTRACTSatellite remote sensing has greatly facilitated the assessment of aboveground biomass in rangelands. Soil-adjusted vegetation indices have been developed to provide better predictions of aboveground biomass, especially for dryland regions. Semi-arid rangelands often complicate a remote sensing based assessment of aboveground biomass due to bright reflecting soils combined with sparse vegetation cover. We aim at evaluating whether soil-adjusted vegetation indices perform better than standard, i.e. unadjusted, vegetation indices in predicting dry aboveground biomass of a saline and semi-arid rangeland in NE-Iran. 672 biomass plots of 2 × 2 m were gathered and aggregated into 13 sites. Generalized Linear Regression Models (GLM) were compared for six different vegetation indices, three standard and three soil-adjusted vegetation indices. Vegetation indices were calculated from the MODIS MCD43A4 product. Model comparison was done using Akaike Information Criterion (AICc), Akaike weights and pseudo R2. Model fits for dry biomass showed that transformed NDVI and NDVI fitted best with R2 = 0.47 and R2 = 0.33, respectively. The optimized soil-adjusted vegetation index (OSAVI) behaved similar to NDVI but less precise. The soil-adjusted vegetation index (SAVI), the modified soil-adjusted vegetation index (MSAVI2) and the enhanced vegetation index (EVI) performed worse than a null model. Hence, soil-adjusted indices based on the soil-line concept performed worse than a simple square root transformation of the NDVI. However, more studies that compare MODIS based vegetation indices for rangeland biomass estimation are required to support our findings. We suggest applying a similar model comparison approach as performed in this study instead of relying on single vegetation indices in order to find optimal relationships with aboveground biomass estimation in rangelands.

Grazing History Effects on Rangeland Biomass, Cover and Diversity Responses to Fire and Grazing Utilization

Exclusion of large grazers from rangelands that evolved with significant grazing pressure can alter natural processes and may have legacy effects by changing magnitude or direction of community responses to subsequent disturbance. Three moderately grazed pastures were paired with 12-ha areas with 15 yr of livestock exclusion. Six treatments were assigned to each in a 2 x 3 factorial arrangement of fire (fall fire or no fire) and grazing utilization (0%, 50%, or 75% biomass removal) to determine grazing history effects on rangeland response to subsequent disturbance. Livestock exclusion increased C3 perennial grass (1 232 vs. 980 ± 50 kg ⋅ ha-1) and forbs (173 vs. 62 ± 19 kg ⋅ ha-1) and reduced C4 perennial grass (36 vs. 180 ± 25 kg ⋅ ha-1) with no effect on total current-year biomass. Diversity was greater in pastures than exclosures (H’ = 1.5400 vs. 1.3823 ± 0.0431). Every biomass, cover, and diversity measure, except subshrub biomass, was affected by fire, grazing utilization, or both. Contrary to expectations, grazing history only interacted with fire effects for old standing dead material and interactions with grazing utilization were limited to old dead, bare ground, richness and dominance. Fire by grazing utilization interaction was limited to bare ground. Fire reduced annual grass (64 vs. 137 ± 29 kg ⋅ ha-1), forbs (84 vs. 133 ± 29 kg ⋅ ha-1), and diversity (H’ = 1.3260 vs. 1.5005 ± 0.0537) with no difference in total current-year biomass (1 557 vs. 1 594 ± 66 kg ⋅ ha-1). Grazing to 75% utilization reduced total current-year biomass (1 467 vs. 1 656 ± 66 kg ⋅ ha-1) and dominance (0.4824 vs. 0.5584 ± 0.0279). Grazing history affected starting points for most variables, but changes caused by grazing utilization or fire were similar between pastures and exclosures, indicating management decisions can be made based on independent knowledge of grazing or fire effects.

Phenology-Based Biomass Estimation to Support Rangeland Management in Semi-Arid Environments

Livestock plays an important economic role in Niger, especially in the semi-arid regions, while being highly vulnerable as a result of the large inter-annual variability of precipitation and, hence, rangeland production. This study aims to support effective rangeland management by developing an approach for mapping rangeland biomass production. The observed spatiotemporal variability of biomass production is utilised to build a model based on ground and remote sensing data for the period 2001 to 2015. Once established, the model can also be used to estimate herbaceous biomass for the current year at the end of the season without the need for new ground data. The phenology-based seasonal cumulative Normalised Difference Vegetation Index (cNDVI), computed from 10-day image composites of the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data, was used as proxy for biomass production. A linear regression model was fitted with multi-annual field measurements of herbaceous biomass at the end of the growing season. In addition to a general model utilising all available sites for calibration, different aggregation schemes (i.e., grouping of sites into calibration units) of the study area with a varying number of calibration units and different biophysical meaning were tested. The sampling sites belonging to a specific calibration unit of a selected scheme were aggregated to compute the regression. The different aggregation schemes were evaluated with respect to their predictive power. The results gathered at the different aggregation levels were subjected to cross-validation (cv), applying a jackknife technique (leaving out one year at a time). In general, the model performance increased with increasing model parameterization, indicating the importance of additional unobserved and spatially heterogeneous agro-ecological effects (which might relate to grazing, species composition, optical soil properties, etc.) in modifying the relationship between cNDVI and herbaceous biomass at the end of the season. The biophysical aggregation scheme, the calibration units for which were derived from an unsupervised ISODATA classification utilising 10-day NDVI images taken between January 2001 and December 2015, showed the best performance in respect to the predictive power (R2cv = 0.47) and the cross-validated root-mean-square error (398 kg·ha−1) values, although it was not the model with the highest number of calibration units. The proposed approach can be applied for the timely production of maps of estimated biomass at the end of the growing season before field measurements are made available. These maps can be used for the improved management of rangeland resources, for decisions on fire prevention and aid allocation, and for the planning of more in-depth field missions.

Determinants of smallholder farmers’ perceptions of impact of climate change on beef production in Malawi

Climate change is projected to intensify and smallholder farmers will be the most affected because they entirely rely on climate-sensitive livelihoods and have low adaptive capacity. Appropriate coping strategies for smallholder farmers depend on an accurate description of the effects of agro-ecological and farmers’ socio-economic factors on climate change. A total of 182 structured questionnaires were administered to determine socio-economic factors affecting smallholder farmers’ perceptions of impacts of climate change on beef production in Malawi. Male heads had a higher perception of having increased cattle feed intake and decreased mortality than female heads suggesting that the former had better control and access to animal feed and health resources. Young farmers had a greater perception of experiencing decreases in cattle water supply than adults implying that they lacked experience required to cope with water scarcity. More educated household heads had a higher perception of experiencing decreased tick loads and increased cattle sales than less educated household heads as they are posited to have increased knowledge of parasite control and understanding of market dynamics and expected to make better marketing decisions. High-income farmers had a lower perception of having decreased rangeland biomass and growth rates than low-income farmers indicating that they had capital to acquire resources required to improve rangeland and cattle production. Gender, age, education and income level were the major socio-economic factors that influenced farmers’ perceptions of impact of climate change on beef production. This highlights the importance of incorporating socio-economic factors when devising climate change adaptation and vulnerability reduction strategies for smallholder beef producers.

Spatio-temporal patterns of herbage availability and livestock movements: A cross-border analysis in the Chinese-Mongolian Altay

Due to increasing population and the recent implementation of policies to intensify the use of land and water resources, the transhumant pastoral systems in the Chinese-Mongolian Altay-Dzungarian region are rapidly changing, leading to modifications of herd size, herd composition and spatial distribution of livestock grazing. This may have major consequences for the supply and quality of rangeland biomass. Despite similar topographic settings, the socio-political framework for Chinese and Mongolian pastoralists differs significantly, leading to differences in rangeland utilization. To substantiate these claims, the long-distance transhumance routes, frequency of pasture changes, daily grazing itineraries and size of pastures were recorded by means of GPS tracking of cattle and goats on 1,535 (China) and 1,396 (Mongolia) observation days. The status quo of the main seasonal pastures was captured by measuring the herbage offer and its nutritive value in 869 sampling spots. In the Altay-Dzungarian region, small ruminant herds covered up to 412 km (Mongolia) and grazed on up to nine pastures per year (China). In Mongolia, the herds’ average duration of stay at an individual pasture was longer than in China, particularly in spring and autumn. Herbage allowance at the onset of a grazing period (kg dry matter per sheep unit and day) ranged from 34/17 to 91/95 (China/Mongolia). Comparing crude protein and phosphorous concentrations of herbage, in China, the highest concentrations were measured for spring and summer pastures, whereas in Mongolia, the highest concentrations were determined for autumn and winter pastures. Based on our data, we conclude that regulation of animal numbers and access to pastures seemingly maintained pasture productivity in China, especially at high altitudes. However, this policy may prohibit flexible adaptation to sudden environmental constraints. In contrast, high stocking densities and grazing of pastures before flowering of herbaceous plants negatively affected rangeland productivity in Mongolia, especially for spring and summer pastures.

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

Abstract. The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011–2013). The sites were divided into three groups (Open plains, Bunch grasses and Spinifex) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: (1) relationships between AGB and vegetation indices combining all sites; (2) separate relationships per site group; (3) multiple regressions including selected vegetation indices per site group; and (4) as in 3 but including rainfall and elevation data. Results indicate that relationships based on single vegetation indices are moderately accurate for green biomass in wide open plains covered with annual grasses. The cross-validation results for green AGB improved for a combination of indices for the Open plains and Bunch grasses sites, but not for Spinifex sites. When rainfall and elevation data are included, cross validation improved slightly with a Q2 of 0.49–0.72 for Open plains and Bunch grasses sites respectively. Cross validation results for total AGB were moderately accurate (Q2 of 0.41) for Open plains but weak or absent for other site groups despite good calibration results, indicating strong influence of site-specific factors.

Evaporative Fraction as an Indicator of Moisture Condition and Water Stress Status in Semi-Arid Rangeland Ecosystems

Rangeland monitoring services require the capability to investigate vegetation condition and to assess biomass production, especially in areas where local livelihood depends on rangeland status. Remote sensing solutions are strongly recommended, where the systematic acquisition of field data is not feasible and does not guarantee properly describing the spatio-temporal dynamics of wide areas. Recent research on semi-arid rangelands has focused its attention on the evaporative fraction (EF), a key factor to estimate evapotranspiration (ET) in the energy balance (EB) algorithm. EF is strongly linked to the vegetation water status, and works conducted on eddy covariance towers used this parameter to increase the performances of satellite-based biomass estimation. In this work, a method to estimate EF from MODIS products, originally developed for evapotranspiration estimation, is tested and evaluated. Results show that the EF estimation from low spatial resolution over wide semi-arid area is feasible. Estimated EF resulted in being well correlated to field ET measurements, and the spatial patterns of EF maps are in agreement with the well-known climatic and landscape Sahelian features. The preliminary test on rangeland biomass production shows that satellite-retrieved EF as a water availability factor significantly increased the capacity of a remote sensing operational product to detect the variability of the field biomass measurements.

Quantification of aboveground rangeland productivity and anthropogenic degradation on the Arabian Peninsula using Landsat imagery and field inventory data

The productivity of semi-arid rangelands on the Arabian Peninsula is spatially and temporally highly variable, and increasing grazing pressure as well as the likely effects of climatic change further threatens vegetation resources. Using the Al Jabal al Akhdar mountains in northern Oman as an example, our objectives were to analyse the availability and spatial distribution of aboveground net primary production (ANPP) and the extent and causes of vegetation changes during the last decades with a remote sensing approach. A combination of destructive and non-destructive biomass measurements by life-form specific allometric equations was used to identify the ANPP of the ground vegetation (< 50 cm) and the leaf and twig biomass of phanerophytes. The ANPP differed significantly among the life forms and the different plant communities, and the biomass of the sparsely vegetated ground was more than 50 times lower (mean = 0.22 t DM ha − 1 ) than the biomass of phanerophytes (mean = 12.3 t DM ha − 1 ). Among the different vegetation indices calculated NDVI proved to be the best predictor for rangeland biomass. Temporal trend analysis of Landsat satellite images from 1986 to 2009 was conducted using a pixel-based least square regression with the annual maximum Normalized Differenced Vegetation Index (NDVI max) as a dependent variable. Additionally, linear relationships of NDVI max and annual rainfall along the time series were calculated. The extent of human-induced changes was analysed using the residual trends method. A strongly significant negative biomass trend detected for 83% of the study area reflected a decrease in annual rainfall but even without clear evidence of deforestation of trees and shrubs, human-induced vegetation degradation due to settlement activities were also important.

Geospatial Assessment of Grazing Regime Shifts and Sociopolitical Changes in a Mongolian Rangeland

Drastic changes have occurred in Mongolia’s grazing land management over the last two decades, but their effects on rangelands are ambiguous. Temporal trends in Mongolia’s rangeland condition have not been well documented relative to the effects of longterm management changes. This study examined changes in grazing land use and rangeland biomass associated with the transition from the socialist collective to the current management systems in the Tsahiriin tal area of northern Mongolia. Grazing lands in Tsahiriin tal that were formerly managed by the socialist collective are now used by numerous nomadic households with their privately owned herds, although the lands remain publicly owned. Grazing pressure has more than tripled and herd distribution has changed from a few spatially clustered large herds of sheep to numerous smaller herds of multiple species. Landsat image– derived normalized-difference vegetation index estimates suggest that rangeland biomass significantly decreased (P , 0.001) from the collective to the postcollective periods. The observed decrease was significantly correlated with changes in the grazing management system and increased stocking density (P , 0.001), even when potential climate-induced changes were considered. Furthermore, field- and Satellite Pour l’Observation de la Terre imagery–based rangeland assessments in 2007 and 2008 indicate that current rangeland biomass is low. Spatial pattern analyses show that the low biomass is uniform throughout the study site. The observed decrease in rangeland biomass might be further accelerated if current grazing land use continues with no formal rangeland management institution or organized, well-structured efforts by the local herding households.

Ground and satellite based assessment of rangeland management in sub-tropical Argentina

Sustainable rangeland management is an important issue not only in semi-arid areas but also in sub-tropical regions. For optimal utilization and protection of natural resources, spatial information on the current state and grazing intensity are required. In this paper the integration possibilities of remote sensing derived information for an optimized rangeland management in the sub-tropical province of Corrientes, Argentina, are investigated. Multi-temporal Landsat imagery is utilized in order to classify land cover types and grazing intensity. The map legend was produced according to the United Nations/Food and Agriculture Organization Land Cover Classification System with special emphasis on separating different rangeland types and management practices. Grazing intensity categories were defined based on percentage of bare soil, sward height and standing dead material. Following a correlation analysis between spectral information and historical ground truth data, above ground biomass was estimated for rangelands. The relationship between total above ground biomass and NDVI ( r 2 = 0.5, P = 0.05) was significant but weak. However, rangeland biomass estimation excluding the complex influence of non-edible vegetation remained a challenge. Accurate stocking rates calculated for the Empedrado Department indicated that current rates were 16% higher than those calculated only on an area basis.

Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie

Sustainable rangeland stewardship calls for synoptic estimates of rangeland biomass quantity (kg dry matter ha − 1 ) and quality [carbon:nitrogen (C:N) ratio]. These data are needed to support estimates of rangeland crude protein in forage, either by percent (CP c) or by mass (CP m). Biomass derived from remote sensing data is often compromised by the presence of both photosynthetically active (PV) and non-photosynthetically active (NPV) vegetation. Here, we explicitly quantify PV and NPV biomass using HyMap hyperspectral imagery. Biomass quality, defined as plant C:N ratio, was also estimated using a previously published algorithm. These independent algorithms for forage quantity and quality (both PV and NPV) were evaluated in two northern mixed-grass prairie ecoregions, one in the Northwestern Glaciated Plains (NGGP) and one in the Northwestern Great Plains (NGP). Total biomass (kg ha − 1 ) and C:N ratios were mapped with 18% and 8% relative error, respectively. Outputs from both models were combined to quantify crude protein (kg ha − 1 ) on a pasture scale. Results suggest synoptic maps of rangeland vegetation mass (both PV and NPV) and quality may be derived from hyperspectral aerial imagery with greater than 80% accuracy.