Analysis of spatial and temporal patterns of aboveground net primary productivity in the Eurasian steppe region from 1982 to 2013

To explore the importance of the Eurasian steppe region (EASR) in global carbon cycling, we analyzed the spatiotemporal dynamics of the aboveground net primary productivity (ANPP) of the entire EASR from 1982 to 2013. The ANPP in the EASR was estimated from the Integrated ANPP_NDVI model, which is an empirical model developed based on field‐observed ANPP and long‐term normalized difference vegetation index (NDVI) data. The optimal composite period of NDVI data was identified by considering spatial heterogeneities across the study area in the Integrated ANPP_NDVI model. EASR's ANPP had apparent zonal patterns along hydrothermal gradients, and the mean annual value was 43.78 g C m^?2 yr^?1, which was lower than the global grasslands average. Compared to other important natural grasslands, EASR's ANPP was lower than the North American, South American, and African grasslands. The total aboveground net primary productivity (TANPP) was found to be 378.97 Tg C yr^?1, which accounted for 8.18%–36.03% of the TANPP for all grasslands. In addition, EASR's TANPP was higher than that of the grasslands in North America, South America, and Africa. The EASR's TANPP increased in a fluctuating manner throughout the entire period of 1982–2013. The increasing trend was greater than that for North American and South American and was lower than that for African grasslands over the same period. The years 1995 and 2007 were two turning points at which trends in EASR's TANPP significantly changed. Our analysis demonstrated that the EASR has been playing a substantial and progressively more important role in global carbon sequestration. In addition, in the development of empirical NDVI‐based ANPP models, the early–middle growing season averaged NDVI, the middle–late growing season averaged NDVI and the annual maximum NDVI are recommended for use for semi‐humid regions, semi‐arid regions, and desert vegetation in semi‐arid regions, respectively.     

Environmental Controls of Primary Production in Agricultural Systems of the Argentine Pampas

We studied the aboveground net primary productivity (ANPP) of wheat crops in the Argentine Pampas. Our specific objectives were to determine (a) the response of ANPP to changes in water availability (b) the regional patterns of ANPP and (c) the interannual variability and environmental controls of ANPP. We used ground and satellite data to address these questions. Wheat ANPP was calculated as the ratio between grain yield and harvest index. We developed a simple model that took into account environmental and genetic improvement effects upon harvest index. We used the normalized difference vegetational index (NDVI) as a surrogate for ANPP at the county level. Straight-line regression models were fitted to single-year and average values of ANPP and precipitation to derive temporal and spatial models for wheat. For grasslands, we used spatial and temporal models already published. At any given site, there was no difference between modeled wheat and grassland average ANPP. The response of ANPP to changes in interannual water availability decreased along the precipitation gradient when vegetation structure (for example, species composition, density, and total cover) was held constant (wheat crops). Wheat ANPP and total production variability, estimated from remotely sensed data, decreased as mean annual precipitation (MAP) increased. The percentage of soils without drainage problems was the variable that explained most of the wheat ANPP spatial variability as shown by stepwise linear regression. Precipitation variability accounted for 49% of wheat ANPP variability. Remotely sensed estimates of ANPP variability showed lower and wheat ANPP higher temporal variability than annual precipitation.     

Estimation of primary production of subhumid rangelands from remote sensing data

Abstract. Above‐ground Net Primary Production (ANPP) is the main determinant of forage availability and hence of stocking density. A tool to track the seasonal and interannual changes in ANPP at the paddock level will be very important for livestock management. We studied the relationship between field ANPP data and the Normalized Difference Vegetation Index (NDVI) for rangelands of the Flooding Pampa of Argentina using spectral data provided by sensors on board of two satellites: NOAA/AVHRR and Landsat TM. The relationship between NDVI and ANPP was linear both for data derived from NOAA/AVHRR and Landsat TM. Changes in ANPP accounted for a large proportion of the temporal and spatial variation of NDVI: 71% of NOAA/AVHRR data and 74% of Landsat TM data. By inverting these models, ANPP may be inferred from NDVI data at a seasonal and paddock scale. NOAA/AVHRR data captured better the seasonal variation in ANPP and were less sensitive to local variations than Landsat TM data. In contrast, Landsat TM data were more sensitive to inter‐site differences in primary production, except for the winter months. Thus, combining information from these two sources offers a good alternative for monitoring rangeland production at high temporal and spatial resolution.     

Seasonal Variation in Aboveground Production and Radiation-use Efficiency of Temperate rangelands Estimated through Remote Sensing

Aboveground net primary production (ANPP) of grasslands varies spatially and temporally. Spectral information provided by remote sensors is a promising new tool that may be able to estimate ANPP in real time and at low cost. The objectives of this study were (a) to evaluate at a seasonal scale the relationship between ANPP and the normalized difference vegetation index (NDVI), (b) to estimate seasonal variations in the coefficient of conversion of absorbed radiation into aboveground biomass (ε_a), and (c) to identify the environmental controls on such temporal changes. We used biomass-based field determinations of ANPP for two grassland sites in the Flooding Pampa, Argentina, and related them with NDVI data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR) satellites using three different models. Results were compared with data obtained from the new Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at an additional site. The first model was based solely on NDVI; the second was based on the amount of photosynthetically active radiation absorbed by the green vegetation (APAR_g), which was derived from NDVI and incoming photosynthetically active radiation (PAR); the third was based on APAR_g and ε_a, which was in turn estimated from climatic variables. NDVI explained between 63 and 93% of ANPP variation, depending on the site considered. Estimates of ANPP were not improved by considering the variation in incoming PAR. At both sites, ε_a varied seasonally (from 0.2 to 1.2 g DM/MJ) and was significantly associated with combinations of precipitation and temperature. Combining ε_a variations with APAR_g increased our ability to account for seasonal ANPP variations at both sites. Our results indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ε_a are correlated throughout the seasons. Thus, in most cases, seasonal variations of ε_a associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.     

Accuracy of the AVHRR vegetation index as a predictor of biomass,primary productivity and net CO2 flux

The Normalized Difference Vegetation Index (NDVI) or greenness index, based on the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA-7 satellite, has been widely interpreted as a measure of regional to global vegetation patterns. This study provides the first rigorous, quantitative evaluation of global relationships between the NDVI and geographically representative vegetation data-bases, including field metabolic measurements and carbon-balance results from global simulation models. Geographic reliability of the NDVI is judged by comparing NDVI values for different surface types with a general global trend and by statistical analysis of relationships to biomass amounts, net and gross primary productivity, and actual evapotranspiration. NDVI data appear to be relatively reliable predictors of primary productivity except in areas of complex terrain, for seasonal values at high latitudes, and in extreme deserts. The strength of the NDVI-productivity relationship seems comparable to that of earlier climate-based productivity models. Little consistent relationship was found, across different vegetation types, between NDVI and biomass amounts or net biospheric CO₂ flux.Abbreviations AET= Actual Evapotranspiration - AVHRR= Advanced Very High Resolution Radiometer - GPP= Gross Primary Production - GVI= Global Vegetation Index - NDVI= Normalized Difference Vegetation Index - NPP= Net Primary Production     

Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes

Aims 1. To characterize ecosystem functioning by focusing on above‐ground net primary production (ANPP), and 2. to relate the spatial heterogeneity of both functional and structural attributes of vegetation to environmental factors and landscape structure. We discuss the relationship between vegetation structure and functioning found in Patagonia in terms of the capabilities of remote sensing techniques to monitor and assess desertification. Location Western portion of the Patagonian steppes in Argentina (39°30′ S to 45°27′ S). Methods We used remotely‐sensed data from Landsat TM and AVHRR/NOAA sensors to characterize vegetation structure (physiognomic units) and ecosystem functioning (ANPP and its seasonal and interannual variation). We combined the satellite information with floristic relevés and field estimates of ANPP. We built an empirical relationship between the Landsat TM‐derived normalized difference vegetation index (NDVI) and field ANPP. Using stepwise regressions we explored the relationship between ANPP and both environmental variables (precipitation and temperature surrogates) and structural attributes of the landscape (proportion and diversity of different physiognomic classes (PCs)). Results PCs were quite heterogeneous in floristic terms, probably reflecting degradation processes. Regional estimates of ANPP showed differences of one order of magnitude among physiognomic classes. Fifty percent of the spatial variance in ANPP was accounted for by longitude, reflecting the dependency of ANPP on precipitation. The proportion of prairies and semideserts, latitude and, to a lesser extent, the number of PCs within an 8 × 8 km cell accounted for an additional 33% of the ANPP variability. ANPP spatial heterogeneity (calculated from Landsat TM data) within an 8 × 8 km cell was positively associated with the mean AVHRR/NOAA NDVI and with the diversity of physiognomic classes. Main conclusions Our results suggest that the spatial and temporal patterns of ecosystem functioning described from ANPP result not only from water availability and thermal conditions but also from landscape structure (proportion and diversity of different PCs). The structural classification performed using remotely‐sensed data captured the spatial variability in physiognomy. Such capability will allow the use of spectral classifications to monitor desertification.     

Use of climatic data and satellite imagery to model the abundance of Culicoides imicola, the vector of African horse sickness virus, in Morocco

African horse sickness (AHS) is a vector-borne, infectious disease of equids caused by African horse sickness virus (AHSV) . The only proven field vector of the virus is the biting midge Culicoides imicola. Following a recent epizootic (1989–91) of AHS in Morocco, light traps and automatic weather stations were operated for 2 years at twenty-two sites distributed over much of the country. The annually-averaged mean daily trap catch of C. imicola at these sites was negatively correlated with wind speed, and positively correlated with the average and mean annual minimum NDVI (Normalized Difference Vegetation Index, a remotely sensed measure of vegetation activity). There were no significant correlations between the mean daily trap catch and air temperature, soil temperature, relative humidity, saturation deficit, rainfall, altitude or the mean annual maximum or range of NDVI. The best two-variable model, which combined Windspeed_MnAvMn (the average daily minimum wind speed of the least windy month) and NDVI_min (the average annual minimum NDVI) as predictors, explained over 50% of the variance in the annually-averaged mean daily trap catch of C. imicola. There was a significant, positive correlation between minimum wind speed at night and the daily mortality rate of adult female C. imicola and it is suggested that the relationship between wind speed and the abundance of C. imicola arises from effects on adult mortality or dispersal. Considering several climatic variables, in North Africa NDVI_min was most significantly correlated with total annual rainfall. It is suggested that the relationship between NDVI_min and the abundance of C. imicola arises from the impact of soil moisture on both. It is proposed that areas of Morocco with higher levels of soil moisture in late summer or autumn provide more, larger and/or more enduring breeding sites for C. imicola, as well as supporting more photosynthetically active vegetation and hence having higher NDVI.     

Detecting Leaf Pulvinar Movements on NDVI Time Series of Desert Trees: A New Approach for Water Stress Detection

Heliotropic leaf movement or leaf ‘solar tracking’ occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (ΔNDVI_mo-mi) and between winter and summer (ΔNDVI_W-S). In this paper, we showed that the ΔNDVI_mo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ΔNDVI_W-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ΔNDVI_mo-mi and ΔNDVI_W-S. For an 11-year time series without rainfall events, Landsat ΔNDVI_W-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ΔNDVI_mo-mi and ΔNDVI_W-S have potential to detect early water stress of paraheliotropic vegetation.     

黄土高原不同植被覆被类型NDVI对气候变化的响应

植被与气候是目前研究生态与环境的重要内容。为探究黄土高原地区植被与气候因子之间的响应机制,利用线性趋势分析、Pearson相关分析、多元线性回归模型以及通径分析的方法,对黄土高原2000—2015年全区和不同植被覆被类型区内NDVI与气候因子的变化趋势以及相互作用关系进行分析。植被覆被分类数据和植被指数数据分别来源于ESA CCI-LC(The European Space Agency Climate Change Initiative Land Cover)以及MODND1T/NDVI(Normalized Difference Vegetation Index)。结果表明:(1) 2000—2015年黄土高原全区植被年NDVI_(max)显著增加的区域占总面积的74.25%,不同植被覆被类型年NDVI_(max)分别为常绿阔叶林常绿针叶林落叶阔叶林落叶针叶林镶嵌草地农田镶嵌林地草地灌木,并且都呈显著增加趋势,其中常绿阔叶林和农田增加幅度最大,为0.012/a。(2)黄土高原全区NDVI与气温、日照、降水和相对湿度等气候因子之间没有显著相关性,但在不同植被覆被类型区,气候因子对NDVI存在显著作用,且不同植被覆被类型差异明显。(3)在全区和不同植被覆被类型区NDVI仅对降水的响应比较一致,气温无论在整个区域尺度还是不同植被覆被类型区对植被的影响均不显著。(4)常绿阔叶林、落叶阔叶林、常绿针叶林及镶嵌林地等以乔木为主的植被覆被类型受年均相对湿度和年总日照时数的显著负效应驱动,草地、镶嵌草地等以草本为主的植被覆被类型则受到年总降水量的显著正效应影响。这说明对植被类型进行区分,更有利于揭示气候对植被的作用机制。     

Remote sensing of protected areas to derive baseline vegetation functioning characteristics

Question: How can we derive baseline/reference situations to evaluate the impact of global change on terrestrial ecosystem functioning? Location: Main biomes (steppes to rain forests) of Argentina. Methods: We used AVHRR/NOAA satellite data to characterize vegetation functioning. We used the seasonal dynamics of the Normalized Difference Vegetation Index (NDVI), a linear estimator of the fraction of the photosynthetic active radiation intercepted by vegetation (f_PAR), and the surface temperature (Ts), for the period 1981–1993. We extracted the following indices: NDVI integral (NDVI‐I), NDVI relative range (R_rel), NDVI maximum value (V_max), date of maximum NDVI (D_max) and actual evapotranspiration. Results: f _PAR varied from 2 to 80%, in relation to changes in net primary production (NPP) from 83 to 1700 g.m‐².yr^‐1. NDVI‐I, Vmax and f_PAR had positive, curvilinear relationships to mean annual precipitation (MAP), NPP was linearly related to MAP. Tropical and subtropical biomes had a significantly lower seasonality (R_rel) than temperate ones. D_max was not correlated with the defined environmental gradients. Evapotranspiration ranged from 100 to 1100 mm.yr^‐1. Interannual variability of NDVI attributes varied across the temperature and precipitation gradients. Conclusions: Our results may be used to represent baseline conditions in evaluating the impact of land use changes across environmental gradients. The relationships between functional attributes and environmental variables provide a way to extrapolate ecological patterns from protected areas across modified habitats and to generate maps of ecosystem functioning.     

Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago

Numerous ecological studies, including of the polar environment, are now using the remotely sensed normalized difference vegetation index (NDVI, e.g. PAL-NDVI or MODIS-NDVI) as a proxy of vegetation productivity rather than performing direct vegetation assessments. Even though previous data strongly suggested a saturation of NDVI at high biomass values, few studies have explicitly included this characteristic in the modelling process. Here, we developed a generalized non-linear model to explicitly model the relationship between temporal variations of NDVI (Pathfinder AVHRR Land 8 km dataset) and empirical field data. We illustrated our approach on the Kerguelen archipelago by using a green biomass index (point-intercept protocol) sampled at a small scale relative to PAL-NDVI data, and in presence of spatial (water) and temporal (cloud contamination, snow) heterogeneity, i.e. field conditions encountered in many ecological studies. We showed a strong relationship (r _pred.obs = 0.89 [0.77; 0.95]_95%) between this index and the seasonal component of NDVI time series (NDVI_comp). Despite the absence of lignified species in the stand, the NDVI_comp reached an asymptote (0.54 ± 0.05) for high values of green biomass index stressing the need to account for non-linearity when relating NDVI and plant measurements. We provided here a new methodological framework to standardize comparisons between studies assessing performance of NDVI as a proxy of vegetation data.

Integrating Remote Sensing and GIS for Prediction of Winter Wheat (Triticum aestivum) Protein Contents in Linfen (Shanxi), China

In this study, relationships between normalized difference vegetation index (NDVI) and plant (winter wheat) nitrogen content (PNC) and between PNC and grain protein content (GPC) were investigated using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data at the different stages of winter wheat in Linfen (Shanxi, P. R. China). The anticipating model for GPC of winter wheat was also established by the approach of NDVI at the different stages of winter wheat. The results showed that the spectrum models of PNC passed F test. The NDVI_4.14 regression effect of PNC model of irrigated winter wheat was the best, and that in dry land was NDVI_4.30. The PNC of irrigated and dry land winter wheat were significantly (P<0.01) and positively correlated to GPC. Both of protein spectral anticipating model of irrigated and dry land winter wheat passed a significance test (P<0.01). Multiple anticipating models (MAM) were established by NDVI from two periods of irrigated and dry land winter wheat and PNC to link GPC anticipating model. The coefficient of determination R² (R) of MAM was greater than that of the other two single-factor models. The relative root mean square error (R_RMSE) and relative error (RE) of MAM were lower than those of the other two single-factor models. Therefore, test effects of multiple proteins anticipating model were better than those of single-factor models. The application of multiple anticipating models for predication of protein content (PC) of irrigated and dry land winter wheat was more accurate and reliable. The regionalization analysis of GPC was performed using inverse distance weighted function of GIS, which is likely to provide the scientific basis for the reasonable winter wheat planting in Linfen city, China.     

Convergence and contingency in production–precipitation relationships in North American and South African C4 grasslands

Mesic grasslands in North America and South Africa share many structural attributes, but less is known of their functional similarities. We assessed the control of a key ecosystem process, aboveground net primary production (ANPP), by interannual variation in precipitation amount and pattern via analysis of data sets (15- and 24-year periods) from long-term research programs on each continent. Both sites were dominated by C₄ grasses and had similar growing season climates; thus, we expected convergence in precipitation–ANPP relationships. Lack of convergence, however, would support an alternative hypothesis—that differences in evolutionary history and purportedly greater climatic variability in South Africa fundamentally alter the functioning of southern versus northern hemisphere grasslands. Neither mean annual precipitation nor mean ANPP differed between the South African and North American sites (838 vs. 857 mm/year, 423.5 vs. 461.4 g/m² respectively) and growing season precipitation–ANPP relationships were similar. Despite overall convergence, there were differences between sites in how the seasonal timing of precipitation affected ANPP. In particular, interannual variability in precipitation that fell during the first half of the growing season strongly affected annual ANPP in South Africa (P < 0.01), but was not related to ANPP in North America (P = 0.098). Both sites were affected similarly by late season precipitation. Divergence in the seasonal course of available soil moisture (chronically low in the winter and early spring in the South African site vs. high in the North American site) is proposed as a key contingent factor explaining differential sensitivity in ANPP to early season precipitation in these two grasslands. These long-term data sets provided no support for greater rainfall, temperature or ANPP variability in the South African versus the North American site. However, greater sensitivity of ANPP to early season precipitation in the South African grassland suggests that future patterns of productivity may be more responsive to seasonal changes in climate compared with the North American site.     

Terrestrial biosphere model performance for inter‐annual variability of land‐atmosphere CO2 exchange

Interannual variability in biosphere‐atmosphere exchange of CO₂ is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter‐annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long‐term measurements of biosphere‐atmosphere CO₂ exchange made with eddy‐covariance flux towers at 11 forested sites in North America. Instead of focusing on model‐data agreement we take a systematic, variability‐oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.     

Intra-annual rainfall variability and grassland productivity: can the past predict the future?

Precipitation quantity has been shown to influence grassland aboveground net primary productivity (ANPP) positively whereas experimental increases in of temporal variability in water availability commonly exhibit a negative relationship with ANPP. We evaluated long term ANPP datasets from the Konza Prairie Long Term Ecological Research (LTER) program (1984–1999) to determine if similar relationships could be identified based on patterns of natural variability (magnitude and timing) in precipitation. ANPP data were analyzed from annually burned sites in native mesic grassland and productivity was partitioned into graminoid (principally C₄ grasses) and forb (C₃ herbaceous) components. Although growing season precipitation amount was the best single predictor of total and grass ANPP (r ²=0.62), several measures of precipitation variability were also significantly and positively correlated with productivity, independent of precipitation amount. These included soil moisture variability, expressed as CV, for June (r ²=0.45) and the mean change in soil moisture between weekly sampling periods in June and August (%wv) (r ²=0.27 and 0.32). In contrast, no significant relationships were found between forb productivity and any of the precipitation variables (p>0.05). A multiple regression model combining precipitation amount and both measures of soil moisture variability substantially increased the fit with productivity (r ²=0.82). These results were not entirely consistent with those of short-term manipulative experiments in the same grassland, however, because soil moisture variability was often positively, not negatively related to ANPP. Differences in results between long and short term experiments may be due to low variability in the historic precipitation record compared to that imposed experimentally as experimental levels of variability exceeded the natural variability of this dataset by a factor of two. Thus, forecasts of ecosystem responses to climate change (i.e. increased climatic variability), based on data constrained by natural and recent historical rainfall patterns may be inadequate for assessing climate change scenarios if precipitation variability in the future is expected to exceed current levels.     

Shifts in the dynamics of productivity signal ecosystem state transitions at the biome‐scale

Understanding ecosystem dynamics and predicting directional changes in ecosystem in response to global changes are ongoing challenges in ecology. Here we present a framework that links productivity dynamics and ecosystem state transitions based on a spatially continuous dataset of aboveground net primary productivity (ANPP) from the temperate grassland of China. Across a regional precipitation gradient, we quantified spatial patterns in ANPP dynamics (variability, asymmetry and sensitivity to rainfall) and related these to transitions from desert to semi‐arid to mesic steppe. We show that these three indices of ANPP dynamics displayed distinct spatial patterns, with peaks signalling transitions between grassland types. Thus, monitoring shifts in ANPP dynamics has the potential for predicting ecosystem state transitions in the future. Current ecosystem models fail to capture these dynamics, highlighting the need to incorporate more nuanced ecological controls of productivity in models to forecast future ecosystem shifts.     

Frequency and Extent of Water Limitation to Primary Production in a Mesic Temperate Grassland

The frequency and extent of water limitation to aboveground net primary production (ANPP) in a mesic grassland in NE Kansas (Konza Prairie, USA) was assessed with an 8-year irrigation experiment. Since 1991, transects spanning upland and lowland sites in annually burned, ungrazed tallgrass prairie were provided with supplemental water to satisfy evapotranspirational demands. This protocol minimized water limitations during the growing season, as well as interannual variability in water stress. Irrigation of this mesic grassland increased ANPP in 6 of 8 years by an average of 26% when compared to control transects. Although interannual variation in ANPP was greater in uplands than lowlands at nominal levels of precipitation, reducing interannual variability in water availability via irrigation eliminated topographic differences; the irrigation protocol also reduced interannual variability in ANPP by as much as 40%. The addition of supplemental water enabled us to extend the relationship between annual precipitation and ANPP in grasslands to precipitation levels (average, 1153 mm; maximum, 1346 mm) similar to those experienced by more mesic grasslands that today exist only as remnants several hundred kilometers east of Kansas. This relationship was linear (r ²= 0.81), with maximum ANPP (738 g/m²) similar to values reported for sites in Illinois and Wisconsin. After 8 years of irrigation, production of the C₃ forb component was twice that in control sites. These results indicate that water limitations in grasslands at the western edge of the presettlement extent of tallgrass prairie affect ANPP in most years and that this high frequency of water limitation may lead to greater dominance of the C₄ grasses than is seen in more eastern grassland sites. Received 18 January 2000; accepted 19 July 2000.     

Spatial variability of above‐ground net primary production in Uruguayan grasslands: a remote sensing approach

Question: How does above‐ground net primary production (ANPP) differ (estimated from remotely sensed data) among vegetation units in sub‐humid temperate grasslands? Location: Centre‐north Uruguay. Methods: A vegetation map of the study area was generated from LANDSAT imagery and the landscape configuration described. The functional heterogeneity of mapping units was analysed in terms of the fraction of photosynthetically active radiation absorbed by green vegetation (fPAR), calculated from the normalized difference vegetation index (NDVI) images provided by the moderate resolution imaging spectroradiometer (MODIS) sensor. Finally, the ANPP of each grassland class was estimated using NDVI and climatic data. Results: Supervised classification presented a good overall accuracy and moderate to good average accuracy for grassland classes. Meso‐xerophytic grasslands occupied 45% of the area, Meso‐hydrophytic grasslands 43% and Lithophytic steppes 6%. The landscape was shaped by a matrix of large, unfragmented patches of Meso‐xerophytic and Meso‐hydrophytic grasslands. The region presented the lowest anthropic fragmentation degree reported for the Rio de la Plata grasslands. All grassland units showed bimodal annual fPAR seasonality, with spring and autumn peaks. Meso‐hydrophytic grasslands showed a radiation interception 10% higher than the other units. On an annual basis, Meso‐hydrophytic grasslands produced 3800 kg dry matter (DM) ha^?1 yr^?1 and Meso‐xerophytic grasslands and Lithophytic steppes around 3400 kg·DM·ha^?1·yr^?1. Meso‐xerophytic grasslands had the largest spatial variation during most of the year. The ANPP temporal variation was higher than the fPAR variability. Conclusions: Our results provide valuable information for grazing management (identifying spatial and temporal variations of ANPP) and grassland conservation (identifying the spatial distribution of vegetation units).     

Environmental controls of NDVI dynamics in Patagonia based on NOAA-AVHRR satellite data

Abstract. Variation in vegetation in extra-Andean Patagonia (Argentina) was analyzed using spectral data derived from AVHRR/NOAA satellite. The study of seasonal dynamics of the Normalized Difference Vegetation Index (NDVI, i.e. a combined index of the reflection in the red and infrared bands) highlighted similarities in functional aspects between regional vegetation units which are dissimilar in a geographical, physiognomical and/or floristical way, and also suggested that gross primary production is correlated with mean annual rainfall. The first axis in a Principal Component Analysis of NDVI data was correlated (r² = 0.90) with NDVI as integrated for the study period. The second axis was correlated (r² = 0.50) with the differences in NDVI during the growing season, reflecting seasonality. Mean annual rainfall accounted for 60% of integrated NDVI variability among vegetation units. Much of the residual variance (62%) was accounted for by the inverse of the distance to the Atlantic Ocean, which is interpreted as an ocean effect on vegetation functioning in the extra-Andean Patagonia.     

Effects of Animal Husbandry on Secondary Production and Trophic Efficiency at a Regional Scale

Agricultural systems are expected to have higher net secondary production (NSP) than natural systems as a result of higher trophic efficiency and lower interannual variability. These differences, however, have not been quantified across regional gradients. We compiled a dataset of herbivore biomass, consumption, NSP, annual precipitation, and aboveground net primary production (ANPP) for extensive livestock farms across a wide precipitation gradient in Argentina. We compared these data with worldwide published studies of natural systems. In a double-logarithmic scale, NSP of agricultural systems increased with ANPP from semiarid to subhumid systems and decreased from subhumid to humid systems, a response that contrasted with the linear positive increase of natural systems. Compared to natural systems dominated by homeotherms, E _troph (NSP:ANPP) in agricultural systems in semiarid areas was 8 times higher, due to a 2 times higher E _consump (Consumption:ANPP) and a 4 times higher E _prod (NSP:Consumption). In subhumid areas, E _troph was 46 times higher, due to a 13.7 times higher E _consump and a 3.3 times higher E _prod. In humid areas, E _troph was 5 times higher, due to a 2.5 times higher E _consump and a 2 times higher E _prod. The interannual variation of herbivore biomass, a major determinant of NSP, was 60 % lower in agricultural than in natural systems dominated by homeotherms, and was decoupled from the variability of precipitation. Agricultural systems reach higher NSP by (1) diverting a major proportion of ANPP from the detritus to the grazing chain, (2) converting more efficiently consumption into NSP, and (3) stabilizing herbivore biomass across years.