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Consistent information on agricultural land use provides a fundamental basis for sustainable land management, achieving zero hunger (SDG2) and maintaining life on land (SDG15) in South Asia and Nepal in particular due to its high vulnerability to natural disasters caused by its diverse geo-climatic system. The present study aims to characterize different crop types (i.e., Maize, Sugarcane, and Wheat) cultivated in Sudurpashchim Province, one of Nepal’s most heavily cultivated areas, and identify their spectral behavior using Sentinel-2 MSI multitemporal data acquired between January and December 2021. In this regard, forty crop profiles were identified based on a 250-m crop-type map provided by the National Soil Science Research Center (NSSRC). Leaf Area Index (LAI), Fractional Vegetation Cover (FVC), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), in combination with Normalized Difference Vegetation Index (NDVI) and Shortwave Infrared bands (SWIR1 and SWIR2), were derived for each crop profile, then averaged per crop type to assess the overall trend. The results revealed the efficiency of biophysical indicators in crop type identification during their growing season. While shortwave infrared bands partially failed to characterize different cropping systems, LAI and FVC performed well in terms of separating wheat from other crops in the February-March period, revealed by maximum values reaching 1.2 and 0.4; then a sudden drop to 0.5 and 0.17 for LAI and FVC, respectively, during the harvesting season. For sugarcane, peak values have been observed during the July-September period, with LAI between 0.8 and 1 and FVC of approximately 0.4. Although a less unusual behavior has been registered for maize, local maxima for LAI, FVC, NDVI, and FAPAR around the July-August period have been found. This was followed by a notable decrease in September, which is contemporary with the harvesting season. Despite the fact that the increasing and decreasing trends of biophysical parameters are relatively synchronous with crop calendars, the distinction between different crop profiles can be robustly improved by adding more profiles and using Sentinel-1 SAR to take advantage of weather insusceptibility, which was a limiting factor for Sentinel-2 MSI.

The Russian forces invaded Ukraine on 24th February 2022 leading to widespread disruption of Ukraine's agricultural system. Ukraine is a major exporter of crops , the invasion therefore poses a significant risk to global food security. Quantifying the extent of this impact is critical, and requires monitoring of Ukraine’s agricultural lands. Total production is one of the prime indicators in this regard. Production in turn is directly proportional to the total harvested area. Harvested areas at regional scales have previously been estimated from satellite data. The majority of these studies use a complete satellite derived phenological time series and make the assumption that senescence leads to harvest. Both these conditions are not applicable in this case, as harvest estimates are required in-season and all planted fields would not necessarily be harvested due to the conflict . A delayed harvest also results in a long browning phase prior to harvest, making it particularly difficult to differentiate from post-harvest signatures. Given these constraints and challenges, we developed a method to monitor crop harvest near-real time using high resolution Planet satellite imagery. Our method includes training a model to cluster change patterns on historic data and then identify harvest patterns in the current season. Samples used to train the model consist of information from two consecutive images. Such samples are collected across the season and spatially across four agro-climatic zones, ensuring we capture a complete representation of change patterns that exist. Clusters are assigned as ‘harvested’ or ‘non-harvested’ by visually inspecting imagery at a higher temporal resolution, using which, harvest can be seen as a clear change event. On clusters which are not fully separable, we apply a hierarchical approach to further separate them. Our method works in the absence of extensive training labels and does not use predefined thresholds or assumptions. We applied the method across the harvesting period for winter crops in Ukraine. Contrary to initial reports and expectations we found a higher percentage of harvested fields in Ukraine. In free Ukraine we found 94% of planted winter crops to be harvested and in occupied Ukraine it was 88% as of 19th September 2022. Strong visual patterns of non-harvested crops were observed along the occupation borders in eastern and southern Ukraine. Harvesting trends in the north and south were largely unaffected by the conflict. With no possibility to collect ground samples, we visually interpreted satellite imagery at a higher temporal frequency to generate statistically significant validation data for model accuracy calculation. We obtained an overall accuracy of 85% with an f1-score of 90% for the harvested class and 73% for the non-harvested class. Our assessments and analysis were directed to different organizations and agencies dealing with the Ukraine crisis and led to several key insights and derived interpretations.Following NASA EarthObservatory article was published based on this work: https://earthobservatory.nasa.gov/images/150590/larger-wheat-harvest-in-ukraine-than-expected

Hurtt, George C., Louise Chini, Ritvik Sahajpal, Steve Frolking, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, et al. 2020. ‘Harmonization of Global Land Use Change and Management for the Period 850–2100 (LUH2) for CMIP6’. Geoscientific Model Development 13 (11): 5425–64. https://doi.org/10.5194/gmd-13-5425-2020.

Terrestrial ecosystems play a critical role in the global carbon cycle but have highly uncertain future dynamics. Ecosystem modelling that includes the scaling-up of underlying mechanistic ecological processes has the potential to improve the accuracy of future projections, while retaining key process-level detail. Over the past two decades, multiple modelling advances have been made to meet this challenge, including the Ecosystem Demography (ED) model and its derivatives including ED2 and FATES. Here, we present the global evaluation of the Ecosystem Demography model (ED v3.0), which likes its predecessors features the formal scaling of physiological processes of individual-based vegetation dynamics to ecosystem scales, together with integrated submodules of soil biogeochemistry and soil hydrology, while retaining explicit tracking of vegetation 3-D structure. This new version builds on previous versions and provides the first global calibration and evaluation, global tracking of the effects of climate and land-use change on vegetation 3-D structure, new spin-up process and input datasets, as well as numerous other advances. Model evaluation was performed with respect to a set of important benchmarking datasets, and model estimates were within observational constraints for multiple key variables including: (i) global patterns of dominant plant functional types (broadleaf vs evergreen); (ii) spatial distribution, seasonal cycle, and interannual trends of global Gross Primary Production (GPP); (iii) global interannual variability of Net Biome Production (NBP); and (iv) global patterns of vertical structure including leaf area and canopy height. With this global model version, it is now possible to simulate vegetation dynamics from local to global scales and from seconds to centuries, with a consistent mechanistic modelling framework amendable to data from multiple traditional and new remote sensing sources, including lidar.

The GEOGLAM Crop Monitor for the G20 Agricultural Market Information System (AMIS) provides a public good of open, timely, science-driven information on crop conditions in support of market transparency. It reflects an international, multi-source, consensus assessment of crop growing conditions, status, and agro-climatic factors likely to impact global production, focusing on the major producing and trading countries for the four primary crops monitored by AMIS (wheat, maize, rice, and soybean). This initiative, developed in response to a request by AMIS, is fulfilling the demand from the international community for a timely and transparent consensus on global crop prospects. Since its launch, the Crop Monitor for AMIS has grown extensively and has become an internationally recognized source of information on global crop prospects, widely quoted by public and private agencies as well as top tier media. It represents the first time that the international community comes together on a monthly basis to produce joint crop assessments. This paper provides a historical and institutional perspective on the creation and role of the GEOGLAM Crop Monitor in fulfilling the objectives of AMIS including a description of its methods and protocols and, ultimately how decision-makers are using its information.

Times of higher paleolake levels in Mono Lake basin correspond to higher abundances of authigenic minerals such as calcite and Mg-smectite in the Wilson Creek Formation, the lake sediments exposed around the modern lake that represent the persistent wetter conditions of the last glacial cycle. It has been suggested that precipitation of these minerals in Mono Lake is controlled by the flux of water (surface and ground), which replenishes Ca2+ and Mg2+ ions in the lake. This water is subsequently depleted due to the high rates of evaporation in the Mono Basin, resulting in precipitation of calcite and Mg-smectite mineral phases. Thermodynamic evaporation models starting with Sierra Nevada spring water can simulate the chemical composition of Mono Lake remarkably well. These models do not, however, consider the mixing of freshwaters in the lake that is hypothesized to result in precipitation of calcite and Mg-smectite. Here, we present the results of our empirical evaporation and mixing (E&M) model using simple thermodynamic approaches. Although this model is highly simplified, it provides a valuable test of the hypothesis.

Crop yield information at field scale is important for farmers, crop insurance companies and agricultural communities in general. In this study, a wide range of ground-collected yield data was used to develop crop yield forecast models for the two internationally important crops: wheat and soybeans. A deep neural network (NN), a long short-term memory (LSTM), was trained for both crops individually. For each crop, the LSTM model was trained for two different scenarios including using Synthetic Aperture Radar (SAR)-only data as first scenario and using integration of SAR and optical satellite data as a second scenario. The root mean square error (RMSE) and coefficient of determination $(R^{2})$ were estimated for each scenario. The results demonstrated that the accuracies improved from RMSE of 516.7 kg/ha and $R^{2}$ of 0.79 (scenario 1) to RMSE of 433.77 kg/ha and $R^{2}$ of 0.87 (scenario 2) for soybeans. For wheat, the accuracies improved from RMSE of 617.14 kg/ha and $R^{2}$ of 0.83 (scenario 1) to RMSE of 423.04 kg/ha and $R^{2}$ of 0.87 (scenario 2). These results show that using SAR data and their integration with optical satellite data is a promising approach for crop yield forecast at field scale.

Climate mitigation and forest management require accurate information on carbon stocks, fluxes, and potential future sequestration potential. Previous large-scale estimates have substantial uncerta...

On 10 August 2020, a series of intense and fast-moving windstorms known as a derecho caused widespread damage across Iowa’s (the top US corn-producing state) agricultural regions. This severe weather event bent and flattened crops over approximately one-third of the state. Immediate evaluation of the disaster’s impact on agricultural lands, including maps of crop damage, was critical to enabling a rapid response by government agencies, insurance companies, and the agricultural supply chain. Given the very large area impacted by the disaster, satellite imagery stands out as the most efficient means of estimating the disaster impact. In this study, we used time-series of Sentinel-1 data to detect the impacted fields. We developed an in-season crop type map using Harmonized Landsat and Sentinel-2 data to assess the impact on important commodity crops. We intersected a SAR-based damage map with an in-season crop type map to create damaged area maps for corn and soybean fields. In total, we identified 2.59 million acres as damaged by the derecho, consisting of 1.99 million acres of corn and 0.6 million acres of soybean fields. Also, we categorized the impacted fields to three classes of mild impacts, medium impacts and high impacts. In total, 1.087 million acres of corn and 0.206 million acres of soybean were categorized as high impacted fields.

Information on historical land-cover change is important for understanding human impacts on the environment. Over the last decade, global models have characterized historical land-use changes, but few have been able to relate these changes with corresponding changes in land-cover. Utilizing the latest global land-use change data, we make several assumptions about the relationship between land-use and land-cover change, and evaluate each scenario with remote sensing data to identify optimal fit. The resulting transition rule can guide the incorporation of land-cover information within earth system models.

Charcoal is a key energy source for urban households in sub-Saharan Africa and charcoal production is the main cause of forest degradation across the region. We used multitemporal high-resolution remote sensing optical imagery to quantify the extent and intensity of forest degradation associated with charcoal production and its impact on forest carbon stocks for the main supplying area of an African capital. This analysis documents the advance of forest degradation and quantifies its aboveground biomass removals over a seven-year period, registering that, between 2013 and 2016, the average annual area under charcoal production was 103 km2 and the annual aboveground biomass removals reached 1081 000 (SD = 2461) Mg. Kiln densities in the study area rose to 2 kilns-ha, with an average of 90.7 Mg ha−1 of extracted aboveground biomass. Charcoal production was responsible for the degradation of 55.5% of the mopane woodlands in the study area between 2013 and 2019. We estimated post-disturbance recovery times using an ecosystem model calibrated for the study area. The simulations showed that recovery times could require up to 150 years for current aboveground biomass extraction rates. The results of the remote sensing analysis and the simulations of the ecosystem model corroborate the unsustainability of the present patterns of charcoal production. The detailed characterization of the spatial and temporal patterns of charcoal production was combined with household survey information to quantify the impact of the urban energy demand of the Maputo urban area on forest carbon stocks. The analysis shows that Maputo charcoal demand was responsible for the annual degradation of up to 175.3 km2 and that the contribution of the study area to this demand fluctuated between 75% and 33% over the study period. The extent, advance pace and distance from urban centers documented in this study support the idea that forest degradation from charcoal production cannot merely be considered a peri-urban process. The intensity of the aboveground biomass (AGB) removals and its contribution to forest carbon stocks changes is significant at the national and regional levels.

We used historical Landsat imagery to monitor forest degradation from charcoal production in the main supplying region of the Mozambican capital, Maputo, during a ten-year period (2008–2018). We applied a change detection method that exploits temporal NDVI dynamics associated with charcoal production. This forest degradation temporal sequence exposes the magnitude and the spatial and temporal dynamics of charcoal production, which is the main forest degradation driver in sub-Saharan Africa. The annual area under charcoal production has been steadily increasing since 2008 and reached 11 673 ha in 2018. The total forest degraded extent in the study area during the 10-year study period covered 79 630 ha, which represents 68% of the available mopane woodlands in 2008. Only 5% of the available mopane woodlands area remain undisturbed in the study area. Total gross carbon emissions associated charcoal production during this 10-year period were estimated in 1.13 Mt. These results mark forest degradation from charcoal production as the main driver of forest cover change in southern Mozambique. They also denote that, while charcoal production may be relatively localized in space, its implications for forest cover change and carbon emissions in a sub-Saharan African context are relevant at larger geographical scales. This study represents a proof of concept of the feasibility of medium resolution Earth observation data to monitor forest degradation from charcoal production in the context of the growing urban energy demand. It also highlights the potential opportunities to improve REDD+ monitoring, reporting and verification efforts in sub-Saharan Africa as a first step toward designing effective management and policy interventions.

Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be span classCombining double low line"inline-formula"37.42×106/span kmspan classCombining double low line"inline-formula"2/span, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

Developed algorithm to select representative crop rotations using multi-year CDLs.Algorithm produces acreage estimates comparable to CDL and agricultural census data.Pronounced shifts from grassland to monoculture row crops detected in US Midwest. Crop rotations (the practice of growing crops on the same land in sequential seasons) reside at the core of agronomic management as they can influence key ecosystem services such as crop yields, carbon and nutrient cycling, soil erosion, water quality, pest and disease control. Despite the availability of the Cropland Data Layer (CDL) which provides remotely sensed data on crop type in the US on an annual basis, crop rotation patterns remain poorly mapped due to the lack of tools that allow for consistent and efficient analysis of multi-year CDLs. This study presents the Representative Crop Rotations Using Edit Distance (RECRUIT) algorithm, implemented as a Python software package, to select representative crop rotations by combining and analyzing multi-year CDLs. Using CDLs from 2010 to 2012 for 5 states in the US Midwest, we demonstrate the performance and parameter sensitivity of RECRUIT in selecting representative crop rotations that preserve crop area and capture land-use changes. Selecting only 82 representative crop rotations accounted for over 90% of the spatio-temporal variability of the more than 13,000 rotations obtained from combining the multi-year CDLs. Furthermore, the accuracy of the crop rotation product compared favorably with total state-wide planted crop area available from agricultural census data. The RECRUIT derived crop rotation product was used to detect land-use conversion from grassland to crop cultivation in a wetland dominated part of the US Midwest. Monoculture corn and monoculture soybean cropping were found to comprise the dominant land-use on the newly cultivated lands.

The development of effective measures to stabilize atmospheric CO2 concentration and mitigate negative impacts of climate change requires accurate quantification of the spatial variation and magnitude of the terrestrial carbon (C) flux. However, the spatial pattern and strength of terrestrial C sinks and sources remain uncertain. In this study, we designed a spatially-explicit agroecosystem modeling system by integrating the Environmental Policy Integrated Climate (EPIC) model with multiple sources of geospatial and surveyed datasets (including crop type map, elevation, climate forcing, fertilizer application, tillage type and distribution, and crop planting and harvesting date), and applied it to examine the sensitivity of cropland C flux simulations to two widely used soil databases (i.e. State Soil Geographic—STATSGO of a scale of 1:250,000 and Soil Survey Geographic—SSURGO of a scale of 1:24,000) in Iowa, USA. To efficiently execute numerous EPIC runs resulting from the use of high resolution spatial data (56 m), we developed a parallelized version of EPIC. Both STATSGO and SSURGO led to similar simulations of crop yields and Net Ecosystem Production (NEP) estimates at the State level. However, substantial differences were observed at the county and sub-county (grid) levels. In general, the fine resolution SSURGO data outperformed the coarse resolution STATSGO data for county-scale crop-yield simulation, and within STATSGO, the area-weighted approach provided more accurate results. Further analysis showed that spatial distribution and magnitude of simulated NEP were more sensitive to the resolution difference between SSURGO and STATSGO at the county or grid scale. For over 60% of the cropland areas in Iowa, the deviations between STATSGO- and SSURGO-derived NEP were larger than 1 Mg C ha− 1 yr− 1, or about half of the average cropland NEP, highlighting the significant uncertainty in spatial distribution and magnitude of simulated C fluxes resulting from differences in soil data resolution.

Terrestrial ecosystems and their vegetation are linked to climate. With the potential of accelerated climate change from anthropogenic forcing, there is a need to further evaluate the transient response of ecosystems, their vegetation, and their influence on the carbon balance, to this change. The equilibrium response of ecosystems to climate change has been estimated in previous studies in global domains. However, research on the transient response of terrestrial vegetation to climate change is often limited to domains at the sub-continent scale. Estimation of the transient response of vegetation requires the use of mechanistic models to predict the consequences of competition, dispersal, landscape heterogeneity, disturbance, and other factors, where it becomes computationally prohibitive at scales larger than sub-continental. Here, we used a pseudo-spatial ecosystem model with a vegetation migration sub-model that reduced computational intensity and predicted the transient response of vegetation and carbon to climate change in northern North America. The ecosystem model was first run with a current climatology at half-degree resolution for 1000 years to establish current vegetation and carbon distribution. From that distribution, climate was changed to a future climatology and the ecosystem model run for an additional 2000 simulation years. A model experimental design with different combinations of vegetation dispersal rates, dispersal modes, and disturbance rates produced 18 potential change scenarios. Results indicated that potential redistribution of terrestrial vegetation from climate change was strongly impacted by dispersal rates, moderately affected by disturbance rates, and marginally impacted by dispersal mode. For carbon, the sensitivities were opposite. A potential transient net carbon sink greater than that predicted by the equilibrium response was estimated on time scales of decades&ndash, centuries, but diminished over longer time scales. Continued research should further explore the interactions between competition, dispersal, and disturbance, particularly in regards to vegetation redistribution.

NASA Carbon Monitoring System projects NNX12AN07G, NNX14AP12G, and 80NSSC17K0710; National Science Foundation Grant No. DGE1322106

Funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund., Disturbances, both natural and anthropogenic, are critical determinants of forest structure, function, and distribution. The vulnerability of forests to potential changes in disturbance rates remains largely unknown. Here, we developed a framework for quantifying and mapping the vulnerability of forests to changes in disturbance rates. By comparing recent estimates of observed forest disturbance rates over a sample of contiguous US forests to modeled rates of disturbance resulting in forest loss, a novel index of vulnerability, Disturbance Distance, was produced. Sample results indicate that 20% of current US forestland could be lost if disturbance rates were to double, with southwestern forests showing highest vulnerability. Under a future climate scenario, the majority of US forests showed capabilities of withstanding higher rates of disturbance then under the current climate scenario, which may buffer some impacts of intensified forest disturbance.

A comparative assessment of six alternative cropping systems over 20 years shows that, once well established, successional herbaceous vegetation grown on marginal lands has a direct greenhouse gas emissions mitigation capacity that rivals that of purpose-grown crops. Productive agricultural land that could otherwise be used to produce much-needed food crops is being diverted towards grain-based ethanol production in both Europe and the United States, partly in response to government legislation. An alternative is to grow cellulosic crops on so-called marginal lands. An evaluation of the potential of marginal lands in the Midwestern United States to produce biofuel while mitigating direct greenhouse gas emissions now finds that they have the capacity to produce a significant amount of biofuel energy without the initial carbon debt and indirect land-use costs associated with food-based biofuels. Legislation on biofuels production in the USA1 and Europe2,3 is directing food crops towards the production of grain-based ethanol2,3, which can have detrimental consequences for soil carbon sequestration4, nitrous oxide emissions5, nitrate pollution6, biodiversity7 and human health8. An alternative is to grow lignocellulosic (cellulosic) crops on ‘marginal’ lands9. Cellulosic feedstocks can have positive environmental outcomes10,11 and could make up a substantial proportion of future energy portfolios12,13. However, the availability of marginal lands for cellulosic feedstock production, and the resulting greenhouse gas (GHG) emissions, remains uncertain. Here we evaluate the potential for marginal lands in ten Midwestern US states to produce sizeable amounts of biomass and concurrently mitigate GHG emissions. In a comparative assessment of six alternative cropping systems over 20 years, we found that successional herbaceous vegetation, once well established, has a direct GHG emissions mitigation capacity that rivals that of purpose-grown crops (−851 ± 46 grams of CO2 equivalent emissions per square metre per year (gCO2e m−2 yr−1)). If fertilized, these communities have the capacity to produce about 63 ± 5 gigajoules of ethanol energy per hectare per year. By contrast, an adjacent, no-till corn–soybean–wheat rotation produces on average 41 ± 1 gigajoules of biofuel energy per hectare per year and has a net direct mitigation capacity of −397 ± 32 gCO2e m−2 yr−1; a continuous corn rotation would probably produce about 62 ± 7 gigajoules of biofuel energy per hectare per year, with 13% less mitigation. We also perform quantitative modelling of successional vegetation on marginal lands in the region at a resolution of 0.4 hectares, constrained by the requirement that each modelled location be within 80 kilometres of a potential biorefinery. Our results suggest that such vegetation could produce about 21 gigalitres of ethanol per year from around 11 million hectares, or approximately 25 per cent of the 2022 target for cellulosic biofuel mandated by the US Energy Independence and Security Act of 2007, with no initial carbon debt nor the indirect land-use costs associated with food-based biofuels. Other regional-scale aspects of biofuel sustainability2, such as water quality11,14 and biodiversity15, await future study.

There are strong relationships between climate and ecosystems. With the prospect of anthropogenic forcing accelerating climate change, there is a need to understand how terrestrial vegetation responds to this change as it influences the carbon balance. Previous studies have primarily addressed this question using empirically based models relating the observed pattern of vegetation and climate, together with scenarios of potential future climate change, to predict how vegetation may redistribute. Unlike previous studies, here we use an advanced mechanistic, individually based, ecosystem model to predict the terrestrial vegetation response from future climate change. The use of such a model opens up opportunities to test with remote sensing data, and the possibility of simulating the transient response to climate change over large domains. The model was first run with a current climatology at half-degree resolution and compared to remote sensing data on dominant plant functional types for northern North America for validation. Future climate data were then used as inputs to predict the equilibrium response of vegetation in terms of dominant plant functional type and carbon redistribution. At the domain scale, total forest cover changed by ~2% and total carbon storage increased by ~8% in response to climate change. These domain level changes were the result of much larger gross changes within the domain. Evergreen forest cover decreased 48% and deciduous forest cover increased 77%. The dominant plant functional type changed on 58% of the sites, while total carbon in deciduous vegetation increased 107% and evergreen vegetation decreased 31%. The percent of terrestrial carbon from deciduous and evergreen plant functional types changed from 27%/73% under current climate conditions, to 54%/46% under future climate conditions. These large predicted changes in vegetation and carbon in response to future climate change are comparable to previous empirically based estimates, and motivate the need for future development with this mechanistic model to estimate the transient response to future climate changes.

The goal of this project undertaken by GLBRC (Great Lakes Bioenergy Research Center) Area 4 (Sustainability) modelers is to develop a national capability to model feedstock supply, ethanol production, and biogeochemical impacts of cellulosic biofuels. The results of this project contribute to sustainability goals of the GLBRC; i.e. to contribute to developing a sustainable bioenergy economy: one that is profitable to farmers and refiners, acceptable to society, and environmentally sound. A sustainable bioenergy economy will also contribute, in a fundamental way, to meeting national objectives on energy security and climate mitigation. The specific objectives of this study are to: (1) develop a spatially explicit national geodatabase for conducting biofuel simulation studies and (4) locate possible sites for the establishment of cellulosic ethanol biorefineries. To address the first objective, we developed SENGBEM (Spatially Explicit National Geodatabase for Biofuel and Environmental Modeling), a 60-m resolution geodatabase of the conterminous USA containing data on: (1) climate, (2) soils, (3) topography, (4) hydrography, (5) land cover/ land use (LCLU), and (6) ancillary data (e.g., road networks, federal and state lands, national and state parks, etc.). A unique feature of SENGBEM is its 2008-2010 crop rotation data, a crucially important component for simulating productivity andmore » biogeochemical cycles as well as land-use changes associated with biofuel cropping. ARRA support for this project and to the PNNL Joint Global Change Research Institute enabled us to create an advanced computing infrastructure to execute millions of simulations, conduct post-processing calculations, store input and output data, and visualize results. These computing resources included two components installed at the Research Data Center of the University of Maryland. The first resource was 'deltac': an 8-core Linux server, dedicated to county-level and state-level simulations and PostgreSQL database hosting. The second resource was the DOE-JGCRI 'Evergreen' cluster, capable of executing millions of simulations in relatively short periods. ARRA funding also supported a PhD student from UMD who worked on creating the geodatabases and executing some of the simulations in this study. Using a physically based classification of marginal lands, we simulated production of cellulosic feedstocks from perennial mixtures grown on these lands in the US Midwest. Marginal lands in the western states of the US Midwest appear to have significant potential to supply feedstocks to a cellulosic biofuel industry. Similar results were obtained with simulations of N-fertilized perennial mixtures. A detailed spatial analysis allowed for the identification of possible locations for the establishment of 34 cellulosic ethanol biorefineries with an annual production capacity of 5.6 billion gallons. In summary, we have reported on the development of a spatially explicit national geodatabase to conduct biofuel simulation studies and provided simulation results on the potential of perennial cropping systems to serve as feedstocks for the production of cellulosic ethanol. To accomplish this, we have employed sophisticated spatial analysis methods in combination with the process-based biogeochemical model EPIC. The results of this study will be submitted to the USDOE Bioenergy Knowledge Discovery Framework as a way to contribute to the development of a sustainable bioenergy industry. This work provided the opportunity to test the hypothesis that marginal lands can serve as sources of cellulosic feedstocks and thus contribute to avoid potential conflicts between bioenergy and food production systems. This work, we believe, opens the door for further analysis on the characteristics of cellulosic feedstocks as major contributors to the development of a sustainable bioenergy economy.« less

Traditional genetic algorithms with elitist selection are unable to locate more than one solution in a multimodal fitness landscape in a single run. This genetic drift is illustrated in Mapex, a smart spatial analysis technique, employing a genetic algorithm for spatial cluster discovery. However, for detecting multiple clusters Mapex provides a non-ideal approach. In this paper, we use a fitness sharing niching technique along with circle overlap detection to enhance Mapex. The results show the suitability of such an approach to locate solutions in a multimodal domain.

Indian language computing is lagging behind due to several reasons, primary amongst them being the unavailability of suitable interfaces for human-computer interaction. Natural Language Conversational Interfaces (NLCIs) play the all important role in improving the usability of interfaces for the Indian masses. In this paper an NLCI incorporating speech and dialog is presented.