An official website of the United States government
Abstract Soil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth,Lp, and a hillslope diffusion coefficient,D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC‐depth relations and the spatial pattern of remote sensing‐derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes.
more »
« less
This content will become publicly available on May 1, 2026
Mid‐infrared spectroscopy and machine learning improve accessibility of Hawaiʻi soil health assessment
Monitoring soil health is important for sustaining agricultural productivity and ecological integrity around the world. However, current assessment approaches relying on conventional laboratory methods are resource intensive. Mid‐infrared (MIR) soil spectroscopy offers an opportunity to increase assessment throughput and reduce user costs, potentially improving accessibility for land managers and producers. This study aims to develop a high‐throughput, hybridized model for soil health assessment tailored to the diverse agricultural and ecological landscapes of Hawaiʻi, with potential applicability to other subtropical and tropical areas. Leveraging a newly developed spectral dataset (n = 634) and machine learning methods, we predicted inherent mineralogy and intensive land use legacy with 94.5% and 91.4% accuracy, respectively, validated with threefold cross‐validation. Additionally, we predicted four key soil health indicators: total organic carbon (CCC = 0.97), CO2burst (CCC = 0.93), potentially mineralizable nitrogen (CCC = 0.89), and water‐stable mega‐aggregates (CCC = 0.79). These predicted soil features were then used to predict the Hawaiʻi soil health score. Our results demonstrate the potential for MIR spectroscopy to reshape soil health assessment in Hawaiʻi by offering a rapid, cost‐effective alternative to traditional methods. Finally, we discuss the importance of adopting a soil health testing framework to report results that are intuitive for diverse stakeholders, including local producers and land managers.
more »
« less
- Award ID(s):
- 2124922
- PAR ID:
- 10646158
- Publisher / Repository:
- Soil Science Society of America Journal
- Date Published:
- Journal Name:
- Soil Science Society of America Journal
- Volume:
- 89
- Issue:
- 3
- ISSN:
- 0361-5995
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The development of agricultural systems is a fundamental component of social-ecological transformation and a predominant factor influencing social behavior and structuring. However, oversimplification of traditional agricultural production often occurs and limits the understanding of past populations’ abilities to mitigate potential risks and enhance food security through effective land management strategies. The social-ecological traits that characterize the Hawaiian Islands provides a unique vantage to explore human ecodynamics over the longue durée and assess how these systems can be used to inform current and future land-use strategies, both locally and globally. Using the Hawaiian archipelago as a case study, digitized historical maps depicting a range of crop species and cropping systems were georeferenced to assess previous estimates of land use by early island populations and demonstrate the limitations of narratives constructed from previously modeled extents of land-use activity that rely solely on the preservation of archaeological remnants. The results of our mapped vegetation correspond well with the more intensive forms of agriculture that were included in previous models, but overall indicate that previous models do not fully represent the extent of land use by early island populations, missing vast applications of agroforestry and arboriculture. Based on our findings, we argue that the omission of cultivation systems not associated with physical infrastructure has vastly limited the comprehension of land use by early island populations and driven narratives in social-ecological dynamics that underestimate the extent of agricultural production while inferring sociopolitical outcomes based on the prevailing agricultural dichotomy. To remedy this limitation, we suggest a multimethods approach that integrates diverse data sets for an agricultural model that is more inclusive of all agricultural forms implemented by early Native Hawaiian populations and, therefore, is more representative of the extents of land use by island populations.more » « less
-
The Hawaiian Islands have been employed as a model system to reconstruct agroecological extents of traditional Polynesian agricultural production systems. However, the reliability of previously modeled agricultural extents is unknown due to limitations in empirical evidence to assess accuracy. Utilizing a geospatial database of 8,561 archaeological sites compiled by the Hawaiʻi State Historic Preservation Department (SHPD), this research assessed the accuracy and reliability of three spatial models that estimate the extents of traditional Hawaiian agricultural systems. The results of the model sensitivity assessment indicate the three geospatial models capture the spatial patterns and relative extents of intensive agricultural systems with substantial infrastructure, while additional work is needed to assess reliability of modeled agricultural systems with more indefinite infrastructure.more » « less
-
Abstract Generalizable methods that identify suitable aquatic habitat across large river basins and regions are needed to inform resource management. Habitat suitability models intersect environmental variables to predict species occurrence, but are often data intensive and thus are typically developed at small spatial scales. This study estimated mean monthly aquatic habitat suitability throughout Utah (USA) for Bonneville Cutthroat Trout (Oncorhynchus clarkii utah) and Bluehead Sucker (Catostomus discobolus) with publicly available, geospatial datasets. We evaluated 15 habitat suitability models using unique combinations of percent of mean annual discharge, velocity, gradient, and stream temperature. Environmental variables were validated with observed conditions and species presence observations to verify habitat suitability estimates. Stream temperature, gradient, and discharge best predicted Bonneville Cutthroat Trout presence, and gradient and discharge best predicted Bluehead Sucker presence. Simple aquatic habitat suitability models outperformed models that used only streamflow to estimate habitat for both species, and are useful for conservation planning and water resources decision‐making. This modeling approach could enable resource managers to prioritize stream restoration across vast regions within their management domain, and is potentially compatible with water management modeling to improve ecological objectives in management models.more » « less
-
Abstract The atmospheric concentration of nitrous oxide (N2O) has increased by 23% since the pre‐industrial era, which substantially destructed the stratospheric ozone layer and changed the global climate. However, it remains uncertain about the reasons behind the increase and the spatiotemporal patterns of soil N2O emissions, a primary biogenic source. Here, we used an integrative land ecosystem model, Dynamic Land Ecosystem Model (DLEM), to quantify direct (i.e., emitted from local soil) and indirect (i.e., emissions related to local practices but occurring elsewhere) N2O emissions in the contiguous United States during 1900–2019. Newly developed geospatial data of land‐use history and crop‐specific agricultural management practices were used to force DLEM at a spatial resolution of 5 arc‐min by 5 arc‐min. The model simulation indicates that the U.S. soil N2O emissions totaled 0.97 ± 0.06 Tg N year−1during the 2010s, with 94% and 6% from direct and indirect emissions, respectively. Hot spots of soil N2O emission are found in the US Corn Belt and Rice Belt. We find a threefold increase in total soil N2O emission in the United States since 1900, 74% of which is from agricultural soil emissions, increasing by 12 times from 0.04 Tg N year−1in the 1900s to 0.51 Tg N year−1in the 2010s. More than 90% of soil N2O emission increase in agricultural soils is attributed to human land‐use change and agricultural management practices, while increases in N deposition and climate warming are the dominant drivers for N2O emission increase from natural soils. Across the cropped acres, corn production stands out with a large amount of fertilizer consumption and high‐emission factors, responsible for nearly two‐thirds of direct agricultural soil N2O emission increase since 1900. Our study suggests a large N2O mitigation potential in cropland and the importance of exploring crop‐specific mitigation strategies and prioritizing management alternatives for targeted crop types.more » « less
