Aboveground Net Primary Productivity Data Research Articles

Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision systems

CONTEXTMonitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVETo develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the “virtual entity” of a digital twin support decision system where the “physical entity” is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODSFortnight ANPP data were calculated from MODIS EVI for the 2001–2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation using climate variables as independent variables. RESULTS AND CONCLUSIONSThe sigmoidal functional response model fit was highly significant for the accumulated ANPP profile. This model also had a high explanatory power for the accumulated ANPP curve. The median of the percentage absolute residuals for forecasts made 1 to 4 fortnights ahead ranged from 17% to 18%. The ANN significantly reduced this unexplained variability in ANPP, showing a median reduction in residuals of 35%, 31%, 30%, and 30% for 1 to 4 fortnights ahead forecasts, respectively, when compared to predictions from the sigmoidal functional response fit. SIGNIFICANCEBy integrating both parametric and machine learning techniques, the hybrid model developed can make accurate predictions in a way that is both efficient and dependable. The hybrid model not only represents an advantage in terms of predictive power, but it also allows for a deeper understanding of the basic ecological processes involved in forage production.

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 9th 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 3rd-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.

Assessing precipitation, evapotranspiration, and NDVI as controls of U.S. Great Plains plant production

AbstractProductivity throughout the North American Great Plains grasslands is generally considered to be water limited, with the strength of this limitation increasing as precipitation decreases. We hypothesize that cumulative actual evapotranspiration water loss (AET) from April to July is the precipitation‐related variable most correlated to aboveground net primary production (ANPP) in the U.S. Great Plains (GP). We tested this by evaluating the relationship of ANPP to AET, precipitation, and plant transpiration (Tr). We used multi‐year ANPP data from five sites ranging from semiarid grasslands in Colorado and Wyoming to mesic grasslands in Nebraska and Kansas, mean annual NRCS ANPP, and satellite‐derived normalized difference vegetation index (NDVI) data. Results from the five sites showed that cumulative April‐to‐July AET, precipitation, and Tr were well correlated (R2: 0.54–0.70) to annual changes in ANPP for all but the wettest site. AET and Tr were better correlated to annual changes in ANPP compared to precipitation for the drier sites, and precipitation in August and September had little impact on productivity in drier sites. April‐to‐July cumulative precipitation was best correlated (R2 = 0.63) with interannual variability in ANPP in the most mesic site, while AET and Tr were poorly correlated with ANPP at this site. Cumulative growing season (May‐to‐September) NDVI (iNDVI) was strongly correlated with annual ANPP at the five sites (R2 = 0.90). Using iNDVI as a surrogate for ANPP, we found that county‐level cumulative April–July AET was more strongly correlated to ANPP than precipitation for more than 80% of the GP counties, with precipitation tending to perform better in the eastern more mesic portion of the GP. Including the ratio of AET to potential evapotranspiration (PET) improved the correlation of AET to both iNDVI and mean county‐level NRCS ANPP. Accounting for how different precipitation‐related variables control ANPP (AET in drier portion, precipitation in wetter portion) provides opportunity to develop spatially explicit forecasting of ANPP across the GP for enhancing decision‐making by land managers and use of grassland ANPP for biofuels.

Modelling aboveground net primary production (ANPP) of an Atlantic mountain grassland based on time series approach

Because primary productivity is related both with the energy that sustains food webs and with species diversity, it is usually considered a key ecosystem property and a reliable indicator of available forage. In this work the aboveground net primary production (ANPP) of an Atlantic mountain grassland system was modelled in order to attempt producing short-term forecasts. Since grazing influences productivity, two treatment levels (grazing and exclusion) were experimentally applied in each of three field sites. Monthly ANPP data were then collected over three consecutive vegetative periods (2006-2008), thereby obtaining six time series (one per plot). Since no significant differences among sites (within treatments) were found, these six series were later reduced through averaging to only two series (one per treatment level). Two kinds of statistical models were then used to attempt monthly ANPP forecasting: exponential smoothing methods and ARIMA models. Both methodologies turned out to produce inadequate forecasts due to the presence of marked local features (innovative outliers) in our relatively short time-series data. Nonetheless, useful information for a more innovative shepherding management was revealed (e.g. the presence of within-year variation in ANPP, and differences between the grazing and exclusion treatments). Longer data series, which would require a more demanding effort in sampling investment, are likely necessary in order to obtain adequate forecasts using these time series methodologies.

Glyphosate Alters Aboveground Net Primary Production, Soil Organic Carbon, and Nutrients in Pampean Grasslands (Argentina)

We have previously demonstrated that recurrent application of glyphosate causes dramatic shift in the vegetation structure of the native grasslands of Flooding Pampa. As these structural changes might alter functional processes such as primary production, carbon, nitrogen and phosphorus cycling, this study aims to evaluate functional changes associated with the application of glyphosate in these temperate grasslands. We measured aboveground net primary production (ANPP) during two consecutive years, and the concentration of organic carbon, nitrogen and phosphorus in the soil during the following six years after primary production measurements ended in glyphosate treated and non- treated (control) paddocks of a commercial livestock far. We related the vegetation data, basal cover, species richness and diversity, obtained in a previous study conducted in the same paddocks of the livestock farm, with ANPP data obtained in this one. Late summer applications of glyphosate greatly reduced the biomass contribution of warm-season perennial grasses and legumes and increased the contribution of cool season annual grasses, altering the seasonal pattern of ANPP. As the reduction of the spring and summer productivity could not be compensated by the increase of cool-season productivity, the annual ANPP was lower in the glyphosate-treated paddocks than in control paddocks. Glyphosate applications also decreased soil organic carbon and phosphorus concentration, probably because of the reduction of ANPP, the changes of its seasonal distribution and the shift in the floristic composition of the community, which may modify the amount and quality of the litter. We found a linear positive relationship between basal cover, species richness and species diversity with ANPP, which suggest that the negative effects on ecosystem functioning would be directly related with the changes in vegetation structure caused by glyphosate application.

Multiscale Trend Analysis for Pampa Grasslands Using Ground Data and Vegetation Sensor Imagery.

This study aimed to evaluate changes in the aboveground net primary productivity (ANPP) of grasslands in the Pampa biome by using experimental plots and changes in the spectral responses of similar vegetation communities obtained by remote sensing and to compare both datasets with meteorological variations to validate the transition scales of the datasets. Two different geographic scales were considered in this study. At the local scale, an analysis of the climate and its direct influences on grassland ANPP was performed using data from a long-term experiment. At the regional scale, the influences of climate on the grassland reflectance patterns were determined using vegetation sensor imagery data. Overall, the monthly variations of vegetation canopy growth analysed using environmental changes (air temperature, total rainfall and total evapotranspiration) were similar. The results from the ANPP data and the NDVI data showed the that variations in grassland growth were similar and independent of the analysis scale, which indicated that local data and the relationships of local data with climate can be considered at the regional scale in the Pampa biome by using remote sensing.

Climatic controls of aboveground net primary production in semi-arid grasslands along a latitudinal gradient portend low sensitivity to warming.

Although climate models forecast warmer temperatures with a high degree of certainty, precipitation is the primary driver of aboveground net primary production (ANPP) in most grasslands. Conversely, variations in temperature seldom are related to patterns of ANPP. Thus forecasting responses to warming is a challenge, and raises the question: how sensitive will grassland ANPP be to warming? We evaluated climate and multi-year ANPP data (67 years) from eight western US grasslands arrayed along mean annual temperature (MAT; ~7-14 °C) and mean annual precipitation (MAP; ~250-500 mm) gradients. We used regression and analysis of covariance to assess relationships between ANPP and temperature, as well as precipitation (annual and growing season) to evaluate temperature sensitivity of ANPP. We also related ANPP to the standardized precipitation evaporation index (SPEI), which combines precipitation and evapotranspiration to better represent moisture available for plant growth. Regression models indicated that variation in growing season temperature was negatively related to total and graminoid ANPP, but precipitation was a stronger predictor than temperature. Growing season temperature was also a significant parameter in more complex models, but again precipitation was consistently a stronger predictor of ANPP. Surprisingly, neither annual nor growing season SPEI were as strongly related to ANPP as precipitation. We conclude that forecasted warming likely will affect ANPP in these grasslands, but that predicting temperature effects from natural climatic gradients is difficult. This is because, unlike precipitation, warming effects can be positive or negative and moderated by shifts in the C3/C4 ratios of plant communities.

Convergence between ANPP estimation methods in grasslands — A practical solution to the comparability dilemma

Aboveground net primary production (ANPP) is a key ecosystem characteristic and of fundamental importance for essentially all aspects of matter and energy fluxes in terrestrial ecosystems. Various methods for estimating ANPP are available and despite partial consensus on ‘best practice methods’ important methodological issues remain unresolved: ANPP data obtained with different methods differ in their magnitude, variability and their tendency to over- or underestimate primary production. Paradoxically, despite the large number of published ANPP data, the limited comparability of ANPP estimates across studies de facto leads to a scarcity of ANPP data for assembled large-scale studies. We aimed to overcome these problems by establishing conversion rates between the most commonly used ANPP methods, making the large body of published ANPP data more comparable and thus useful for assembled large-scale studies.Using seasonal biomass dynamics from 89 sites representing various biomes and climata, we established linear conversions for all 21 combinations between the seven most common ANPP estimation algorithms in grass-dominated vegetation. We also checked for confounding effects of environmental factors such as biome and climatic aridity. Aridity was the only factor with a clear influence on ANPP conversions, and in six cases we thus calculated separate relationships for dry and humid environments. In these cases, dryland ANPP was systematically underestimated by the respective methods. As these methods are insensitive to turn-over processes from live to senescent biomass, we assume this underestimation is related to climate-induced differences in biomass turn-over rates, with more arid sites having higher rates.The majority of the resulting 27 conversions had high (pseudo) R2 values (≥0.65; full range: 0.31–0.92), indicating clear linear relationships between most ANPP estimation methods. Given the large size of the dataset and the accuracy of statistical models, we assume that most conversion formulae are generally valid. We classified conversions with respect to their R2 values and their methodological comparability, and concluded that 16 conversions can be fully recommended. For those cases where a recalculation of ANPP on basis of original biomass data is not possible, our conversion formulae offer an easy and practical approach to synchronize ANPP estimates from divergent algorithms and sources.

Assessment of changes in the state of the rangelands of Inner Mongolia, China between 1998 and 2007 using remotely sensed data

The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998–2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1 km2 of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000–05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments.

Intra‐seasonal precipitation patterns and above‐ground productivity in three perennial grasslands

Summary Relationships between above‐ground net primary productivity (ANPP) of grasslands and annual precipitation are often weak at the site level, with much of the inter‐annual variation in ANPP left unexplained. A potential reason for this is that the distribution of precipitation within a growing season affects productivity in addition to the total amount. We analysed long‐term ANPP data for three southern African temperate grasslands (mean annual precipitation ranging from 538 mm to 798 mm) to determine the effects of precipitation event size, number and spacing relative to seasonal totals. Ungrazed, non‐manipulated treatments at each site showed contrasting results despite sharing a common, dominant species. At the driest site, a model combining average event size and number of events per growing season provided a substantially better fit to the ANPP data than precipitation amount (seasonal total). At the wettest site, the interval between events was the most important precipitation variable. Precipitation distribution was not important at the intermediate site where amount was the best predictor of ANPP. A limit to the size of precipitation events efficiently utilized for ANPP was evident for the driest site only. At each site, experimental treatments that altered species composition and soil fertility had little effect on precipitation–ANPP relationships. The lack of consistency in the relative importance of the precipitation variables among sites suggests that local, edaphic factors modify precipitation–ANPP relationships. This analysis demonstrates that the distribution and size of precipitation events can affect ANPP independent of precipitation amount. As altered precipitation regimes are forecast by global climate models, the sensitivity of ecosystems to precipitation distribution should be considered when predicting responses to climate change. While mean values of precipitation, and other ecosystem drivers, are typically used to predict function at the level of whole ecosystems, our results show that more complex measures of environmental variability may be required to understand ecosystem function, and to increase the accuracy of predictions of ecosystem responses to global change.

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.

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.

The influence of soil texture on the soil water dynamics and vegetation structure of a shortgrass steppe ecosystem

We analyzed soil water data from three sites with different soil textures in the shortgrass steppe of northeastern Colorado, USA. Our objective was to evaluate the relationship between the occurrence of plant functional types and the effect of soil texture on soil water availability. Soil water availability was greatest in the upper soil layers at all three sites, but the loamy sand site had significantly greater soil water availability than the sandy clay loam and sandy clay sites in wetter years at depths below 60 cm. Calculations of proportional water availability by layer using both field data and fifty-year soil water model simulations, showed that the sandy clay loam and sandy clay soils on average had greater water availability in layers 30 cm and above, but that the loamy sand had the greatest water availability in layers beneath this, particularly at 105 cm. This observation can be linked to the occurrence of a fine textured subsoil at this site. The textural pattern in the loamy sand profile effectively creates two water resources: a shallow pool accessible to all plants; and a deep pool accessible only to deep-rooted plants. This is offered as an explanation for the co-dominance of the two main plant functional types at the loamy sand site. At the other two sites, shallow-rooted shortgrass vegetation dominated, being more consistent with the general pattern for the area. Thus the patterns of vegetation structure at the three sites were consistent with the hypothesis. Aboveground net primary production data for the three sites, along with transpiration estimates from the model simulations, indicated that the additional water availability in the coarse textured soil was associated with higher overall plant productivity. Nomenclature: Taxonomic nomenclature follows R. L. McGregor & T. M. Barkley (1986) Flora of the Great Plains. Great Plains Flora Association. University Press of Kansas, Lawrence.