Machine Learning Modeling of Global Soil Profile Organic Carbon Fractions and Δ14C Revealing Divergent Drivers and Carbon Sequestration Potential

Zenodo (CERN European Organization for Nuclear Research) · 2026-04-26

Wisconet: A Case Study in Meteorological Infrastructure Capacity Development

Meteorological Applications · 2026-03-01 · 1 citations

ABSTRACT Meteorological observations with high spatial and temporal resolution are necessary for improved forecasts and early warnings of high impact weather. Meteorological observation infrastructure can be expanded in countries with modernized and developing national meteorological and hydrological services. In the United States, mesoscale weather networks (mesonets) are installed in many states to supplement the existing observational infrastructure, support forecasting and early warnings, and serve multisector stakeholders. Wisconsin's Environmental Mesonet (Wisconet) is one of the newest mesonets in the United States, with 78 weather and soil monitoring stations installed across Wisconsin's 72 counties, and is presented as a case study in expanding meteorological observation infrastructure. Discussed are Wisconet's deployment and maintenance strategy, station design, information technology infrastructure, and considerations for staffing and public outreach. The global applicability of a mesonet deployment for developing meteorological infrastructure capacity is also presented.

Machine Learning Modeling of Global Soil Profile Organic Carbon Fractions and Δ14C Revealing Divergent Drivers and Carbon Sequestration Potential

Zenodo (CERN European Organization for Nuclear Research) · 2026-04-26

Comment on essd-2025-445

2026-02-15

<strong class="journal-contentHeaderColor">Abstract.</strong> Land use-land cover, nutrient inputs from fertilizer and manure, and irrigation are primary anthropogenic drivers of ecosystem functioning and degradation. Historical datasets covering these drivers at various spatial resolutions are essential for analyzing changes in these drivers as well as for input to models that estimate ecosystem outcomes such as water quality and runoff. We describe a new dataset for the conterminous United States (CONUS) &ndash; the Harmonized Land Nutrient Irrigation Dataset (HLNID) &ndash; that leverages existing datasets both at the county-scale (e.g., Census of Agriculture) and higher resolution land change model outputs (e.g., FORE-SCE) and remotely-sensed products (e.g., NLCD, CDL) to produce annual land use-land cover (including crop type), fertilizer and manure nutrient mass (nitrogen and phosphorus), and irrigation extent for the years 1938&ndash;2020. The flexible method can provide data at a range of custom spatial resolutions but we present results at 48 and 250 arc-seconds. The dataset reveals specific changes such as the increase in corn and soybean (replacing small grains and pasture) in the northern Great Plains since the 1990s, the spatial concentration of manure production in certain regions such as the uplands of the Southern Seaboard, and the expansion of irrigation in regions such as the Prairie Gateway. The method can readily incorporate new raw input datasets (e.g., CDL) to create updated versions but is limited by current time lags in state fertilizer sales data reporting.

Diverging Temperature and Apparent Temperature-Based Urban Heat Island of a Midsize City

SSRN Electronic Journal · 2026-01-01

Comment on essd-2025-445

2026-03-02

<strong class="journal-contentHeaderColor">Abstract.</strong> Land use-land cover, nutrient inputs from fertilizer and manure, and irrigation are primary anthropogenic drivers of ecosystem functioning and degradation. Historical datasets covering these drivers at various spatial resolutions are essential for analyzing changes in these drivers as well as for input to models that estimate ecosystem outcomes such as water quality and runoff. We describe a new dataset for the conterminous United States (CONUS) &ndash; the Harmonized Land Nutrient Irrigation Dataset (HLNID) &ndash; that leverages existing datasets both at the county-scale (e.g., Census of Agriculture) and higher resolution land change model outputs (e.g., FORE-SCE) and remotely-sensed products (e.g., NLCD, CDL) to produce annual land use-land cover (including crop type), fertilizer and manure nutrient mass (nitrogen and phosphorus), and irrigation extent for the years 1938&ndash;2020. The flexible method can provide data at a range of custom spatial resolutions but we present results at 48 and 250 arc-seconds. The dataset reveals specific changes such as the increase in corn and soybean (replacing small grains and pasture) in the northern Great Plains since the 1990s, the spatial concentration of manure production in certain regions such as the uplands of the Southern Seaboard, and the expansion of irrigation in regions such as the Prairie Gateway. The method can readily incorporate new raw input datasets (e.g., CDL) to create updated versions but is limited by current time lags in state fertilizer sales data reporting.

Development of historical maps of land use-land cover, crop type, nutrients, and irrigation across CONUS (1938–2020) at different spatial resolutions

Earth system science data · 2025-09-05 · 1 citations

Abstract. Land use-land cover, nutrient inputs from fertilizer and manure, and irrigation are primary anthropogenic drivers of ecosystem functioning and degradation. Historical datasets covering these drivers at various spatial resolutions are essential for analyzing changes in these drivers as well as for input to models that estimate ecosystem outcomes such as water quality and runoff. We describe a new dataset for the conterminous United States (CONUS) – the Harmonized Land Nutrient Irrigation Dataset (HLNID) – that leverages existing datasets both at the county-scale (e.g., Census of Agriculture) and higher resolution land change model outputs (e.g., FORE-SCE) and remotely-sensed products (e.g., NLCD, CDL) to produce annual land use-land cover (including crop type), fertilizer and manure nutrient mass (nitrogen and phosphorus), and irrigation extent for the years 1938–2020. The flexible method can provide data at a range of custom spatial resolutions but we present results at 48 and 250 arcsec. The dataset reveals specific changes such as the increase in corn and soybean (replacing small grains and pasture) in the northern Great Plains since the 1990s, the spatial concentration of manure production in certain regions such as the uplands of the Southern Seaboard, and the expansion of irrigation in regions such as the Prairie Gateway. The method can readily incorporate new raw input datasets (e.g., CDL) to create updated versions but is limited by current time lags in state fertilizer sales data reporting. The data presented in this work are available at https://doi.org/10.5061/dryad.r4xgxd2rh (Booth and Kucharik, 2026).

Supplementary material to "Development of historical maps of land use-land cover, crop type, nutrients, and irrigation across CONUS (1938–2020) at different spatial resolutions"

2025-09-05

Adapting an Agroecosystem Model to Account for Cover Crop Management in the Midwest USA

SSRN Electronic Journal · 2025-01-01

Adapting an agroecosystem model to account for cover crop management in the Midwest USA

Smart Agricultural Technology · 2025-04-04 · 2 citations

• Agro-IBIS simulations matched observed cover crop biomass reasonably well. • Environmental factors affected observed and simulated cover crop biomass similarly. • Agro-IBIS captured interannual variability in cover crop biomass well. • Agro-IBIS is useful for evaluating cover crop establishment and efficacy. Agroecosystem modeling tools can provide insights into cover crop performance under varying environmental and management combinations. This study aims to (1) simulate winter cereal rye cover crops in Agro-IBIS, a process-based terrestrial ecosystem model and (2) evaluate Agro-IBIS performance in predicting aboveground biomass (AGB) of winter cereal rye cover crops. To achieve this, the winter wheat plant functional type (PFT) in Agro-IBIS was adapted to represent winter cereal rye as a cool-season winter annual grass cover crop. We adjusted the specific leaf area (SLA), maximum Rubisco activity at 15 °C (V c ,max ), growing degree days (GDD) base temperature, GDD upper threshold, and planting and termination dates as indicated by observed data. Model performance was evaluated using observed data from continuous maize and maize-soybean rotation systems in southern Wisconsin. The model effectively represented interannual variability of winter cereal rye cover crop AGB that was measured in southern Wisconsin in continuous maize and maize-soybean rotation systems. This demonstrated the efficacy of Agro-IBIS in representing establishment success, cold-hardening, spring green-up, and AGB accumulation of winter cereal rye cover crops in conventional annual grain cropping systems. Environmental drivers like growing season length, accumulated GDDs, precipitation amount, and solar radiation were key drivers of cover crop AGB production, which is generally represented by Agro-IBIS. This suggests the model would be an accurate tool to use when investigating the impact of climate change or increased weather variability on the success of cover crops across the Midwest and beyond.