nasa-gesdisc/es-publications-researchareas huggingface dataset:
Negligible cycling of terrestrial carbon in many lakes of the arid circumpolar landscape (2019)

High-latitude environments store nearly half of the planets below-ground organic carbon (OC), mostly in perennially frozen permafrost soils. Climatic changes drive increased export of terrestrial OC into many aquatic networks, yet the role that circumpolar lakes play in mineralizing this carbon is unclear. Here we directly evaluate ecosystem-scale OC cycling for lakes of interior Alaska. This arid, low-relief lake landscape is representative of over a quarter of total northern circumpolar lake area, but is greatly under-represented in current studies. Contrary to projections based on work in other regions, the studied lakes had a negligible role in mineralizing terrestrial carbon; they received little OC from ancient permafrost soils, and had small net contribution to the watershed carbon balance. Instead, most lakes recycled large quantities of internally derived carbon fixed from atmospheric CO2, underscoring their importance as critical sites for material and energy provision to regional food webs. Our findings deviate from the prevailing paradigm that northern lakes are hotspots of terrestrial OC processing. The shallow and hydrologically disconnected nature of lakes in many arid circumpolar landscapes isolates them from terrestrial carbon processing under current climatic conditions.

Research areas: Ecosystems
Analysis of bloom conditions in fall 2013 in the Strait of Hormuz using satellite observations and model simulations (2017)

In this study an algal bloom event in fall 2013 in the Strait of Hormuz was thoroughly investigated using satellite remote sensing and hydrodynamic modeling. The motivation of this study is to deduce ambient conditions prior to and during the bloom outbreak and understand its trigger. Bloom tracking was achieved by sequential MODIS imagery and numerical simulations. Satellite observations showed that the bloom was initiated in late October 2013 and dissipated in early June 2014. Trajectories of bloom patches were simulated using a Lagrangian transport model. Model-based predictions of bloom patches' trajectories were in good agreement with satellite observations with a probability of detection (POD) reaching 0.85. Analysis of ancillary data, including sea surface temperature, ocean circulation, and wind, indicated that the bloom was likely caused by upwelling conditions in the Strait of Hormuz. Combined with numerical models, satellite observations provide an essential tool for investigating bloom conditions.

Research areas: Water Quality
ENET wild modelling of wild boar distribution and abundance: initial model output based on hunting data and update of occurrence‐based models (2019)

After presenting preliminary models to estimate the habitat suitability for wild boar in MSs and neighbouring countries as a proxy for its relative abundance (i.e. the relative representation of a species in a particular ecosystem, a kind of proxy of the density) the ENETWILD consortium has developed further models for the estimation of wild boar abundance across this extent based on hunting yields (HY). This report therefore presents: i) updated maps of habitat suitability at 10x10 km resolution based on newly available data of wild boar occurrence together with new analysis to test the feasibility of performing such analysis at higher resolution (2x2 km); and, ii) a new model for predicting wild boar relative abundance, also at 10x10 km resolution, using hunting yields. The results of the occurrence model show that more occurrence data are required for specific locations in Eastern Europe in order to ensure robust model prediction of habitat suitability and consequently wild boar distribution. We used the hunting yields model to identify the environmental drivers of species abundance at European scale and fitted separate models for three regions (Southern, Western and Eastern Europe) to predict the distribution of wild boar at 10x10 km resolution. Our initial results highlighted some methodological issues relating to the statistical downscaling that should be taken into account to improve the reliability of the predictions. Whilst the spatial pattern in some areas was similar when comparing the predictions from both the occurrence and abundance models, in other regions there were marked discrepancies. To improve the models it is recommended to i) collect more occurrence data in the NorthEastern region of Europe, in particular on survey effort; ii) combine regional and local hunting records to validate hunting yield predictions to higher spatial resolutions; and, iii) incorporate new environmental variables, especially those closely associated with wild boar abundance and distribution.

Research areas: Ecosystems
Cross-scale evaluation of dynamic crop growth in WRF and Noah-MP-Crop (2020)

Accurately representing croplands in climate models is important for simulating water and energy fluxes between the land and atmosphere, as well as evaluating the impacts of climate change on agriculture. The recent integration of dynamic crop growth in the Noah land surface model with multiparameterization (Noah-MP-Crop) has the potential to substantially advance Earth system modeling and is included in the latest release of the Weather Research and Forecasting (WRF) regional climate model. The addition of dynamic crop growth to WRF provides a unique opportunity to simultaneously evaluate biases associated with a crop model coupled to a regional climate model and address outstanding questions regarding the role of agroecosystems in modulating regional climate. Here, we analyze dynamic crop growth in WRF across three simulated spatial scales (25km, 5km, and 1km) for growing seasons with precipitation above (2010), below (2012), and approximately equal (2015) to the seasonal average. Including dynamic crop growth in WRF significantly reduces biases in simulated leaf area index over croplands in the central U.S. relative to observations. However, there is no substantial difference in calculated daily evapotranspiration, average growing season temperature, or total growing season precipitation between WRF simulations with dynamic crops (WRF-Crop) compared to the dynamic vegetation module without crops (WRF-DV). Simulated corn (soy) mean absolute error (MAE), as a percentage of observed annual average yield, ranges from 24.7% -101% (28.1% - 109%) depending on year and spatial resolution, with the most significant biases in highly irrigated counties. Forcing Noah-MP-Crop with observed climate substantially reduces the range of corn (soy) yield MAE to 9.5% - 55.1% (15.0% - 37.5). Increased model resolution consistently leads to lower corn and soy yield estimates within WRF-Crop.

Research areas: Agriculture
Tiger abundance and gene flow in Central India are driven by disparate combinations of topography and land cover (2017)

AimEffective conservation of the endangered tiger depends upon reliable knowledge of factors driving genetic differentiation and population connectivity. Connectivity models frequently use resistance surfaces not optimized with actual movement or genetic data which limits reliability. Our aim is to use empirical data on genetic diversity of tiger populations to optimize landscape resistance to gene flow and identify factors that predict local population abundance across Central India.LocationThe study area covers 697,000 km2 across Madhya Pradesh and parts of Rajasthan, Jharkhand and Maharashtra.MethodsWe used genetic data of 309 tigers and restricted multivariate optimization of correlation between landscape variables and genetic distance in a reciprocal causal modelling framework to parameterize a resistance surface for gene flow. We further evaluated the association between effective population size and landscape connectivity using all‑subsets logistic regression with model averaging based on AICc.ResultsGene flow is primarily related to topographic roughness and slope position and secondarily to human footprint and land cover. It is much higher in areas of rough topography and ridge tops and is facilitated by forest cover in areas with low human footprint. In contrast, effective population size in protected areas is primarily driven by extent of protected areas and surrounding forest cover, and is not significantly related to resistant kernel connectivity value.Main ConclusionsThis is the first study to use a rigorous multivariate optimization approach to identify factors which limit gene flow of tigers. Tiger movement is highly affected by landscape features, and dispersing tigers move through rough terrain along forested ridges, avoiding non‑forest areas with high human footprint, while local tiger population density is driven primarily by the extent of protected forested habitat. These results have important implications for tiger conservation and can be used to develop empirically supported prioritization of core areas and corridors.

Research areas: Ecosystems
Assessing approaches for stratifying producer fields based on biophysical attributes for regional yield-gap analysis (2020)

Large databases containing producer field-level yield and management records can be used to identify causes of yield gaps. A relevant question is how to account for the diverse biophysical background (i.e., climate and soil) across fields and years, which can confound the effect of a given management practice on yield. Here we evaluated two approaches to group producer fields based on biophysical attributes: (i) a technology extrapolation domain spatial framework (TEDs) that delineates regions with similar (long-term average) annual weather and soil water storage capacity and (ii) clusters based on field-specific soil properties and weather during each crop phase in each year. As a case study, we used yield and management data collected from 3462 rainfed fields sown with soybean across the North Central US (NC-US) during four growing seasons (20142017). Following the TED approach, fields were grouped into 18 TEDs based on the TED that corresponded to the geographic location of each field. In the cluster approach, fields were grouped into clusters based on similarity of in-season weather and soil. To evaluate how the number of clusters would affect the results, fields were grouped separately into 5, 10, 18, and 30 clusters. The two stratification approaches (TEDs and clusters) were compared on their ability to explain the observed yield variation and yield response to key management factors (sowing date and foliar fungicide and/or insecticide). Lack of stratification of producer fields based on their biophysical background ignored management by environment (M  E) interactions, leading to spurious relationships and results that are not relevant at local level. In the case of the cluster approach, a fine stratification (18 and 30 clusters) explained a larger portion of the yield variance compared with a coarse stratification (5 and 10 clusters). However, for our case study in the NC-US region, we did not find strong evidence that the data-rich clustering approach outperformed the TEDs on the ability to explain yield variation and identify M  E interactions. Only the stratification into 30 clusters exhibited a small improved ability at explaining yield variation compared with the TEDs. However, the use of the clustering approach had important trade-offs, including large amount of data requirements and difficulties to scale results to different regions and over time. The choice of the stratification method should be based on objectives, data availability, and expected variation in yield due to erratic weather across regions and years.

Research areas: Agriculture
Water scarcity and fish imperilment driven by beef production (2020)

Human consumption of freshwater is now approaching or surpassing the rate at which water sources are being naturally replenished in many regions, creating water shortage risks for people and ecosystems. Here we assess the impact of human water uses and their connection to water scarcity and ecological damage across the United States, identify primary causes of river dewatering and explore ways to ameliorate them. We find irrigation of cattle-feed crops to be the greatest consumer of river water in the western United States, implicating beef and dairy consumption as the leading driver of water shortages and fish imperilment in the region. We assess opportunities for alleviating water scarcity by reducing cattle-feed production, finding that temporary, rotational fallowing of irrigated feed crops can markedly reduce water shortage risks and improve ecological sustainability. Long-term water security and river ecosystem health will ultimately require Americans to consume less beef that depends on irrigated feed crops.

Research areas: Droughts, Agriculture
A review of global gridded cropping system data products (2021)

Abstract
            Agricultural monitoring, seasonal crop forecasting and climate change adaptation planning all require identifying where, when, how and which crops are grown. Global gridded cropping system data products offer useful information for these applications. However, not only the main sources of information (satellites, censuses, surveys and models) but also the spatial and temporal resolutions of these data products are quite distant from each other because of different user requirements. This is a barrier to strengthening collaborations among the research communities working to increase the capacity of societies to manage climate risks for global food systems, from extreme weather disasters to climate change. A first step is to improve cropping system data products so they can be used more seamlessly across various applications than they are currently. Toward this goal, this article reviews global gridded data products of crop variables (area, yield, cropping intensity, etc) using systematic literature survey, identifies their current limitations, and suggests directions for future research. We found that cropland or crop type mapping and yield or production estimation/prediction together accounted for half of the research objectives of the reviewed studies. Satellite-based data products are dominant at the finer resolution in space and time (<10 km and daily to annual), while model-based data products are found at the coarser resolutions (>55 km and decadal). Census-based data products are seen at intermediate resolutions (1055 km and annual to decadal). The suggested directions for future research include the hybridization of multiple sources of information, improvements to temporal coverage and resolution, the enrichment of management variables, the exploration of new sources of information, and comprehensiveness within a single data product.

Research areas: Agriculture
A systems-level yield gap assessment of maize-soybean rotation under high-and low-management inputs in the Western US Corn Belt using APSIM (2019)

Quantifying yield gaps (potential minus actual yield) and identifying management practices to close those gaps is critical for sustaining high-yielding production systems. The objectives of this study were to: 1) calibrate and validate the APSIM maize and soybean models using local field experimental data and 2) use the calibrated model to estimate and explain yield gaps in the long term as a function of management (high- vs low-input) and weather conditions (wet-warm, wet-cold, dry-warm and dry-cold years) in the western US Corn Belt. The model was calibrated and validated using in-season crop growth data from six maize-soybean rotations obtained in 2014 and 2015 in Kansas, US. Experimental data included two management systems: 1) Common Practices (CP, low-input), wide row spacing, lower seeding rate, and lack of nutrient applications (except N in maize), and 2) Intensified Practices (IP, high-input), narrow rows, high seeding rate, and balanced nutrition. Results indicated that APSIM simulated in-season crop above ground mass and nitrogen (N) dynamics as well yields with a modeling efficiency of 0.75 to 0.92 and a relative root mean square error of 18 to 31%. The simulated maize yield gap across all years was 4.2 and 2.5 Mg ha1 for low- and high-input, respectively. Similarly, the soybean yield gap was 2.5 and 0.8 Mg ha1. Simulation results indicated that the high-input management system had greater yield stability across all weather years. In warm-dry years, yield gaps were larger for both crops and water scenarios. Irrigation reduced yield variation in maize more than in soybean, relative to the rainfed scenario. Besides irrigation, model analysis indicated that N fertilization for maize and narrow rows for soybean were the main factors contributing to yield gains. This study provides a systems level yield gap assessment of maize and soybean cropping system in Western US Corn Belt that can initiate dialogue (both experimental and modeling activities) on finding and applying best management systems to close current yield gaps.

Research areas: Agriculture
Rural-urban connectivity and agricultural land management across the Global South (2020)

Research on how urbanization affects rural agriculture has typically focused on loss of farmland due to urban expansion. However, more distal pathways that could link urbanization to rural agriculture, including enhanced connectivity through rural-urban migration and market access, remain poorly understood. Here, we assess whether greater rural-urban connectivity is associated with changes in agricultural land management across the Global South. Such associations are complex, and thus difficult to measure at this scale. We therefore take a two-step approach to investigate these relationships. First, using a multivariate clustering approach, we define a series of rural-urban connectivity typologies from existing spatial data on land use, demographics, rural market access, and rural population change (as a proxy for outmigration). We examine the variation in key agricultural outcome variables (mean cereal crop yields, % of attainable yields met, and cropping frequency) within the typologies, which shows that greater overall connectivity (market access and population change) is associated with higher cereal yields, yield attainment, and cropping frequency. Second, building on these clustering results, we develop hypotheses about the relationship between rural-urban connectivity and agricultural land use intensity. We then use propensity score matching to test these hypotheses by comparing locations with similar sociodemographic and land use characteristics. When controlling for gross domestic product (GDP) per capita, agricultural land, and population density, rural locations with relatively high market access, negative population change, and greater built-up area have significantly higher mean nitrogen application rates, irrigated areas, and cereal yields across the Global South. Results vary by region, but greater rural-urban connectivity and urban extents are generally associated with higher overall agricultural inputs and yields, particularly in Asia. However, we find little support for a relationship between connectivity and either % attainable yields met or field size. Our findings stress the need to better understand the mechanisms that link urbanization processes and agricultural management at different spatiotemporal scales.

Research areas: Habitat Conversion/Fragmentation
Identifying crop yield gaps with site-and season-specific data-driven models of yield potential (2021)

There is considerable interest and value in identifying the gap between crop yields that have actually been achieved, and yields that could have potentially been achieved. A suite of methods currently exist to estimate the yield potential of a crop, but there are no approaches that predict the site- and season-specific yield potential using datasets that are readily available and easily accessible for farmers. The aim of this study was to fill this need and develop a novel approach to identify crop yield gaps through site- and season-specific models of crop yield potential. The study focused on cotton lint yield, with data from 14 different seasons and 68 different fields from a collection of large, irrigated cotton farms in eastern Australia. This abundance of yield data was then joined with other spatial and temporal datasets that describe yield, such as rainfall, temperature, soil, and management. A quantile random forest machine learning model was then used to model yield at 30 m resolution, where the 95th percentile predictions were treated as the yield potential. The yield gaps at a 30 m resolution were then estimated for all seasons and sites. The results were compared to a more traditional historical maximum yield approach, where no data modelling and only empirical yield data was used to estimate the yield potential. This revealed that there was a general agreement between the two approaches, although the quantile machine learning approach is both site- and season-specific, not just site-specific. Overall, there is a great need for alternative approaches to estimate yield potential and yield gaps, as the approaches currently available possess many limitations. The approach developed in this study has the potential for wide-spread adoption in broadacre cropping systems, and if the causes of yield gaps are identified, could lead to the implementation of management strategies to close them.

Research areas: Agriculture
Remote sensing assessment of available green water to increase crop production in seasonal floodplain wetlands of sub-Saharan Africa (2022)

Producing more food for a growing population requires sustainable crop intensification and diversification, particularly in high-potential areas such as the seasonal floodplain wetlands of sub-Saharan Africa (SSA). With emerging water shortages and concerns for conserving these multi-functional wetlands, a further expansion of the cropland area must be avoided as it would entail increased use of blue water for irrigation and infringe on valuable protected areas. We advocate an efficient use of the prevailing green water on the existing cropland areas, where small-scale farmers grow a single crop of rainfed lowland rice during the wet season. However, soil moisture at the onset of the rains (pre-rice niche) and residual soil moisture after rice harvest (post-rice niche) may suffice to cultivate short-cycled crops. We developed a methodological approach to analyze the potential for green water cultivation in the pre- and post-rice niches in the Kilombero Valley Floodplain in Tanzania, as a representative case for seasonal floodplain wetlands in SSA. The three-step approach used open-access remote sensing datasets to: (i) extract cropland areas; (ii) analyze soil moisture conditions using evaporative stress indices to identify the pre- and post-rice niches; and (iii) quantify the green water availability in the identified niches through actual evapotranspiration (AET). We identified distinct patterns of green water being available both before and after the rice-growing period. Based on the analyses of evaporative stress indices, the pre-rice niche tends to be longer (~70 days with average AET of 2040 mm/10-day) but also more variable (inter-annual variability >30%) than the post-rice niche (~65 days with average AET of 1030 mm/10-day, inter-annual variability <15%). These findings show the large potential for cultivating short-cycled crops beyond the rice-growing period, such as green manure, vegetables, maize, and forage legumes, by shifting a portion of the nonproductive AET flows (i.e., soil evaporation) to productive flows in form of crop transpiration. A cropland area of 1452 to 1637 km2 (5360% of the total cropland area identified of 2730 km2) could be cultivated using available green water in the dry season, which shows the significance of such change for food security, livelihoods, and resilience of the agricultural community in Kilombero. A wider application of the developed approach in this study can help identifying opportunities and guiding interventions and investments towards establishing sustainable intensification and diversification practices in floodplain wetlands in SSA.

Research areas: Agriculture