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This dataset presents spatiotemporal dynamics of phosphorus (P) fertilizer management (application rate, timing, and method) at a 4km × 4 km resolution in agricultural land of the contiguous U.S. from 1850 to 2022. By harmonizing multiple data sources, we reconstructed the county-level crop-specific P fertilizer use history. We then spatialized and resampled P fertilizer use data to 4 km × 4 km gridded maps based on historical U.S. cropland distribution and crop type database developed by Ye et al. (2024). This dataset contains (1) P fertilizer total consumption and mean application rate at the national level (Tabular); (2) P fertilizer consumption of 11 crops at the state level (Tabular); (3) P fertilizer consumption of permanent pasture (Tabular); (4) P fertilizer consumption of non-farm at the state level (Tabular); (5) P fertilizer application rate of 11 crop types at the state level (Tabular); (6) P fertilizer application rate of 11 crop types at the county level (Tabular); (7) P fertilizer application timing ratio at the state level (Tabular); (8) P fertilizer application method ratio at the state level (Tabular); (9) Gridded maps of P fertilizer application rate based on state-level data; (10) and (11) Gridded maps of P fertilizer application rate based on county-level data; (12)-(20) Gridded maps of P fertilizer application rate for each crop. A detailed description of the data development processes, key findings, and uncertainties can be found in Cao, P., Yi, B., Bilotto, F., Gonzalez Fischer, C., Herrero, M., Lu, C.: Crop-specific Management History of Phosphorus fertilizer input (CMH-P) in the croplands of United States: Reconciliation of top-down and bottom-up data sources, is under review for the journal Earth System Science Data (ESSD). https://essd.copernicus.org/preprints/essd-2024-67/#discussion.  This work is supported by the Iowa Nutrient Research Center, the ISU College of Liberal Arts and Sciences Dean's Faculty Fellowship, and NSF CAREER grant (1945036). 

Agricultural activities have been recognized as an important driver of land cover and land use change (LCLUC) and have significantly impacted the ecosystem feedback to climate by altering land surface properties. A reliable historical cropland distribution dataset is crucial for understanding and quantifying the legacy effects of agriculture-related LCLUC. While several LCLUC datasets have the potential to depict cropland patterns in the conterminous US, there remains a dearth of a relatively high-resolution datasets with crop type details over a long period. To address this gap, we reconstructed historical cropland density and crop type maps from 1850 to 2021 at a resolution of 1 km × 1 km by integrating county-level crop-specific inventory datasets, census data, and gridded LCLUC products. Different from other databases, we tracked the planting area dynamics of all crops in the US, excluding idle and fallow farm land and cropland pasture. The results showed that the crop acreages for nine major crops derived from our map products are highly consistent with the county-level inventory data, with a residual less than 0.2×103 ha (0.2 kha) in most counties (>75 %) during the entire study period. Temporally, the US total crop acreage has increased by 118×106 ha (118 Mha) from 1850 to 2021, primarily driven by corn (30 Mha) and soybean (35 Mha). Spatially, the hot spots of cropland distribution shifted from the Eastern US to the Midwest and the Great Plains, and the dominant crop types (corn and soybean) expanded northwestward. Moreover, we found that the US cropping diversity experienced a significant increase from the 1850s to the 1960s, followed by a dramatic decline in the recent 6 decades under intensified agriculture. Generally, this newly developed dataset could facilitate spatial data development, with respect to delineating crop-specific management practices, and enable the quantification of cropland change impacts on the environment. Annual cropland density and crop type maps are available at https://doi.org/10.6084/m9.figshare.22822838.v2 (Ye et al., 2023). 

Abstract. Understanding and assessing the spatiotemporal patterns in crop-specific phosphorus (P) fertilizer management are crucial for enhancing crop yield and mitigating environmental problems. The existing P fertilizer dataset, derived from sales data, depicts an average application rate over total cropland at the county level but overlooks cross-crop variations. Conversely, the survey-based dataset offers crop-specific application details at the state level yet lacks inter-state variability. By reconciling these two datasets, we developed long-term gridded maps to characterize crop-specific P fertilizer application rates, timing, and methods across the contiguous US at a resolution of 4 km × 4 km from 1850 to 2022. We found that P fertilizer application rate over fertilized areas in the US increased from 0.9 g P m−2 yr−1 in 1940 to 1.9 g P m−2 yr−1 in 2022, with substantial variations among crops. However, approximately 40 % of cropland nationwide has remained unfertilized in the recent decade. The hotspots for P fertilizer use have shifted from the southeastern and eastern US to the Midwest and the Great Plains over the past century, reflecting changes in cropland area, crop choices, and P fertilizer use across different crops. Pre-planting (fall and spring) and broadcast application are prevalent among corn, soybean, and cotton in the Midwest and the Southeast, indicating a high P loss risk in these regions. In contrast, wheat and barley in the Great Plains receive the most intensive P fertilization at planting and via non-broadcast application. The P fertilizer management dataset developed in this study can advance our comprehension of agricultural P budgets and facilitate the refinement of best P fertilizer management practices to optimize crop yield and to reduce P loss. Datasets are available at https://doi.org/10.5281/zenodo.10700821 (Cao et al., 2024). 

Abstract Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N₂O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N₂O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process‐based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low‐ambition nitrogen regulation policies, EF would increase from 1.18%–1.22% in 2010 to 1.27%–1.34% by 2050, representing a relative increase of 4.4%–11.4% and exceeding the IPCC tier‐1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%–0.35% (relative increase of 11.9%–17%). In contrast, high‐ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying–wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N₂O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N₂O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio‐economic assessments and policy‐making efforts. 

This dataset contains yearly projections of emission factors (EFs) for fertilizer-induced direct nitrous oxide (N2O) emissions across the global agricultural lands with a spatial resolution of 0.5° × 0.5° from 1990 to 2050. Emission factor (EF) is defined as the amount of N2O emitted per unit of nitrogen (N) fertilizer applied, expressed in percentage (%). They are developed from a hybrid modeling framework, Dym-EF (more details can be found in Li et al., 2024). The framework integrates machine learning approaches with an ensemble of eight process-based models from The Global N2O Model Intercomparison Project phase 2 (NMIP2) to learn the relationship between EF dynamics and multiple environmental factors, such as climate, soil properties, nitrogen fertilizer input, and other agricultural management practices. After the hybrid modeling framework was extensively validated, we applied it to develop EF projections under different nitrogen management policies and climate change scenarios, including future climate data from 37 Global Climate Models (GCMs). The annual median and standard deviation (SD) of EF under each scenario represent the projection median and variability derived from climate input data using the 37 GCMs.The dataset filenames follow the structure: 'Scenario'_'N regulation'_'Median/SD', where 'Scenario' corresponds to the different nitrogen management and climate scenarios (e.g., INMS1, INMS2, and INMS3), 'N regulation' corresponds to the different nitrogen management levels (e.g., BAU, LowNRegul, and MedNRegul), and 'Median/SD' indicates whether the file contains the median (Median) or standard deviation (SD) of the projections. All relevant data and further details can be found in the supplementary materials and the cited references.INMS1: Business-as-usual, Land use regulation: Medium, Diet: Meat & dairy-rich, Ambition level: LowINMS2: Low-nitrogen regulation, Land use regulation: Medium, Diet: Medium meat & dairy, Ambition level: LowINMS3: Medium-nitrogen regulation, Land use regulation: Medium, Diet: Medium meat & dairy, Ambition level: ModerateINMS4: High-nitrogen regulation, Land use regulation: Medium, Diet: Medium meat & dairy, Ambition level: HighINMS5: Best-case, Land use regulation: Strong, Diet: Low meat & dairy, Ambition level: HighINMS6: Best-case “Plus”, Land use regulation: Strong, Diet: Ambitious diet shift and food-loss/waste reductions, Ambition level: HighINMS7: Bioenergy, Land use regulation: Strong, Diet: Low meat & dairy, Ambition level: HighWe developed this data using the “ranger” package in R 4.1.1, which is accessible at https://cran.r-project.org/web/packages/ranger/. The optimization of the two hyperparameters (ntree and mtry) was performed using the ‘caret’ package, available at https://topepo.github.io/caret/.This database is developed by Li, L., C. Lu, W. Winiwarter, H. Tian, J. Canadell, A. Ito, A.K. Jain, S. Kou-Giesbrecht, S. Pan, N. Pan, H. Shi, Q. Sun, N. Vuichard, S. Ye., S. Zaehle, Q. Zhu. Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector Global Change Biology (In Press) 

It is essential to identify the dominant flow paths, hot spots and hot periods of hydrological nitrate-nitrogen (NO3-N) losses for developing nitrogen loads reduction strategies in agricultural watersheds. Coupled biogeochemical transformations and hydrological connectivity regulate the spatiotemporal dynamics of water and NO3-N export along surface and subsurface flows. However, modeling performance is usually limited by the oversimplification of natural and human-managed processes and insufficient representation of spatiotemporally varied hydrological and biogeochemical cycles in agricultural watersheds. In this study, we improved a spatially distributed process-based hydro-ecological model (DLEM-catchment) and applied the model to four tile-drained catchments with mixed agricultural management and diverse landscape in Iowa, Midwestern US. The quantitative statistics show that the improved model well reproduced the daily and monthly water discharge, NO3-N concentration and loading measured from 2015 to 2019 in all four catchments. The model estimation shows that subsurface flow (tile flow + lateral flow) dominates the discharge (70%-75%) and NO3-N loading (77%-82%) over the years. However, the contributions of tile drainage and lateral flow vary remarkably among catchments due to different tile-drained area percentages and the presence of farmed potholes (former depressional wetlands that have been drained for agricultural production). Furthermore, we found that agricultural management (e.g. tillage and fertilizer management) and catchment characteristics (e.g. soil properties, farmed potholes, and tile drainage) play important roles in predicting the spatial distributions of NO3-N leaching and loading. The simulated results reveal that the model improvements in representing water retention capacity (snow processes, soil roughness, and farmed potholes) and tile drainage improved model performance in estimating discharge and NO3-N export at a daily time step, while improvement of agricultural management mainly impacts NO3-N export prediction. This study underlines the necessity of characterizing catchment properties, agricultural management practices, flow-specific NO3-N movement, and spatial heterogeneity of NO3-N fluxes for accurately simulating water quality dynamics and predicting the impacts of agricultural conservation nutrient reduction strategies. 

By integrating multi-source cross-scale inventories and satellite-based datasets, we reconstructed the annual crop density and crop type map (excluding summer idle/fallow, cropland pasture) in the contiguous US at 1km×1km resolution from 1850 to 2021. The annual crop density map depicts the distribution and fraction of cultivated land, while the crop type map displays the corresponding crop type. The developed datasets fill the data gap in lacking of crop type extent and type maps, which can support the environmental assessment and socioeconomic analysis related to agricultural activities. (Supplement to: Shuchao, Ye et al. (2023): Annual time-series 1-km maps of crop area and types in the conterminous US (CropAT-US): cropping diversity changes during 1850-2021.) 

Abstract Increasing food and biofuel demands have led to the cascading effects from cropland expansions, raised fertilizer use, to increased riverine nitrogen (N) loads. However, little is known about the current trade-off between riverine N pollution and crop production due to the lack of predictive understanding of ecological processes across the land-aquatic continuum. Here, we propose a riverine N footprint (RNF) concept to quantify how N loads change along with per unit crop production gain. Using data synthesis and a well-calibrated hydro-ecological model, we find that the RNF within the Mississippi–Atchafalaya River Basin peaked at 1.95 g N kg⁻¹grain during the 1990s, and then shifted from an increasing to a decreasing trend, reaching 0.65 g N kg⁻¹grain in the 2010s. This implies decoupled responses of crop production and N loads to key agricultural activities approximately after 2000, but this pattern varies considerably among sub-basins. Our study highlights the importance of developing a food–energy–water nexus indicator to examine the region-specific trade-offs between crop production and land-to-aquatic N loads for achieving nutrient mitigation goals while sustaining economic gains.