Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “variability analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Validation of Cardiovascular Parameters during NASA's Functional Task Test

Microgravity exposure causes physiological deconditioning and impairs crewmember task performance. The Functional Task Test (FTT) is designed to correlate these physiological changes to performance in a series of operationally-relevant tasks. One of these, the Recovery from Fall/Stand Test (RFST), tests both the ability to recover from a prone position and cardiovascular responses to orthostasis. PURPOSE: Three minutes were chosen for the duration of this test, yet it is unknown if this is long enough to induce cardiovascular responses similar to the operational 5 min stand test. The purpose of this study was to determine the validity and reliability of heart rate variability (HRV) analysis of a 3 min stand and to examine the effect of spaceflight on these measures. METHODS: To determine the validity of using 3 vs. 5 min of standing to assess HRV, ECG was collected from 7 healthy subjects who participated in a 6 min RFST. Mean R-R interval (RR) and spectral HRV were measured in minutes 0-3 and 0-5 following the heart rate transient due to standing. Significant differences between the segments were determined by a paired t-test. To determine the reliability of the 3-min stand test, 13 healthy subjects completed 3 trials of the FTT on separate days, including the RFST with a 3 min stand. Analysis of variance (ANOVA) was performed on the HRV measures. One crewmember completed the FTT before a 14-day mission, on landing day (R+0) and one (R+1) day after returning to Earth. RESULTS VALIDITY: HRV measures reflecting autonomic activity were not significantly different during the 0-3 and 0-5 min segments. RELIABILITY: The average coefficient of variation for RR, systolic (SBP) and diastolic blood pressures during the RFST were less than 8% for the 3 sessions. ANOVA results yielded a greater inter-subject variability (p<0.006) than inter-session variability (p>0.05) for HRV in the RFST. SPACEFLIGHT: Lower RR and higher SBP were observed on R+0 in rest and stand. On R+1, both RR and SBP trended towards preflight rest and stand values. Postflight HRV showed higher LF/HF for rest and stand and lower HFnu during rest. CONCLUSION: These studies show that a 3 min stand delivers repeatable HRV data in the context of this larger series of FTT tests. Spaceflight-induced changes in blood pressure, RR and autonomic function (HRV) are evident from the RFST.

Arzeno, N. M.↗
NASA NTRS2008

Validation of Cardiovascular Parameters During NASA's Functional Task Test

Microgravity-induced physiological changes, including cardiovascular deconditioning may impair crewmembers f capabilities during exploration missions on the Moon and Mars. The Functional Task Test (FTT), which will be used to assess task performance in short and long duration astronauts, consists of 7 functional tests to evaluate crewmembers f ability to perform activities to be conducted in a partial-gravity environment or following an emergency landing on Earth. The Recovery from Fall/Stand Test (RFST) tests both the subject fs ability to get up from a prone position and orthostatic intolerance. PURPOSE: Crewmembers have never become presyncopal in the first 3 min of quiet stand, yet it is unknown whether 3 min is long enough to cause similar heart rate fluctuations to a 5-min stand. The purpose of this study was to validate and test the reliability of heart rate variability (HRV) analysis of a 3-min quiet stand. METHODS: To determine the validity of using 3 vs. 5-min of standing to assess HRV, 7 healthy subjects remained in a prone position for 2 min, stood up quickly and stood quietly for 6 min. ECG and continuous blood pressure data were recorded. Mean R-R interval and spectral HRV were measured in minutes 0-3 and 0-5 following the heart rate transient due to standing. Significant differences between the segments were determined by a paired t-test. To determine the reliability of the 3-min stand test, 13 healthy subjects completed 3 trials of the complete FTT on separate days, including the RFST with a 3-min stand test. Analysis of variance (ANOVA) was performed on the HRV measures. RESULTS: Spectral HRV measures reflecting autonomic activity were not different (p>0.05) during the 0-3 and 0-5 min segment (mean R-R interval: 738+/-74 ms, 728+/-69 ms; low frequency to high frequency ratio: 6.5+/-2.2, 7.7+/-2.7; normalized high frequency: 0.19+/-0.03, 0.18+/-0.04). The average coefficient of variation for mean R-R interval, systolic and diastolic blood pressures in the prone position and stand test were less than 8% for the test sessions. ANOVA results yielded a greater inter-subject variability (p.0.006) than inter-session variability (p>0.05) for HRV in the stand test. CONCLUSION: These studies show that a 3 minute stand delivers repeatable cardiovascular heart rate and BP data in the context of this larger series of tests such as the FTT.

Arzeno, N. M.↗
NASA NTRS1948

Analysis of effect of basic design variables on subsonic axial-flow-compressor performance

A blade-element theory for axial-flow compressors has been developed and applied to the analysis of the effects of basic design variables such as Mach number, blade loading, and velocity distribution on compressor performance. A graphical method that is useful for approximate design calculations is presented. The relations among several efficiencies useful in compressor design are derived and discussed. The possible gains in useful operating range obtainable by the use of adjustable stator blades are discussed and a rapid approximate method of calculating blade-angle resettings is shown by an example. The relative Mach number is shown to be a dominant factor in determining the pressure ratio.

Sinnette, John T , Jr↗
NASA NTRS2007

The XMM-Newton Extended Survey of the Taurus Molecular Cloud (XEST)

The Taurus Molecular Cloud (TMC) is the nearest large star-forming region, prototypical for the distributed mode of low-mass star formation. Pre-main sequence stars are luminous X-ray sources, probably mostly owing to magnetic energy release. Aims. The XMM-Newton Extended Survey of the Taurus Molecular Cloud (EST) presented in this paper surveys the most populated =5 square degrees of the TMC, using the XMM-Newton X-ray observatory to study the thermal structure, variability, and long-term evolution of hot plasma, to investigate the magnetic dynamo, and to search for new potential members of the association. Many targets are also studied in the optical, and high-resolution X-ray grating spectroscopy has been obtained for selected bright sources. Methods. The X-ray spectra have been coherently analyzed with two different thermal models (2-component thermal model, and a continuous emission measure distribution model). We present overall correlations with fundamental stellar parameters that were derived from the previous literature. A few detections from Chandra observations have been added. Results. The present overview paper introduces the project and provides the basic results from the X-ray analysis of all sources detected in the XEST survey. Comprehensive tables summarize the stellar properties of all targets surveyed. The survey goes deeper than previous X-ray surveys of Taurus by about an order of magnitude and for the first time systematically accesses very faint and strongly absorbed TMC objects. We find a detection rate of 85% and 98% for classical and weak-line T Tau stars (CTTS resp. WTTS), and identify about half of the surveyed protostars and brown dwarfs. Overall, 136 out of 169 surveyed stellar systems are detected. We describe an X-ray luminosity vs. mass correlation, discuss the distribution of X-ray-to-bolometric luminosity ratios, and show evidence for lower X-ray luminosities in CTTS compared to WTTS. Detailed analysis (e.g., variability, rotation-activity relations, influence of accretion on X-rays) will be discussed in a series of accompanying papers.

Guedel, M.↗
NASA NTRS1996

A Cross-Industry Analysis of LOFT Debriefings

Line Oriented Simulation (LOS) iss widely used to provide crews opportunities to practice Cockpit Resource Management (CRM) concepts in realistic, challenging simulated flight situations. LOS includes both Line Oriented Flight Training (LOFT), the original "non-jeopardy" form in which the crews are not graded on their performance, and Line Oriented Evaluation (LOE). In LOE, crews are graded, which is required in those airlines that participate in the FAA's Advanced Qualification Program (ALP). How much crews learn in LOS arid take back to the line hinges on the effectiveness of the debriefing that follows the LOS. The simulation itself is a busy, intense experience, and thoughtful discussion afterwards is necessary for the crew to sort out and interpret what happened and why. Instructors are expected to lead briefings in a way that encourages crew members to analyze for themselves their performance in the LOS. Rather than lecturing the crew in the traditional manner on what they did right and wrong, the instructor is expected to facilitate self-discovery and self-critique by the crew. We have recently completed a study in which we observed 36 LOFT briefings at 5 major U.S. airlines. We audiotape recorded the briefings, transcribed the recordings, and coded for more than 70 variables. Our analysis addresses four broad questions: To what extent do instructors attempt to facilitate crew self-analysis? What techniques do instructors use to facilitate and how effective are these techniques? What is the character of crew participation? To what extent do briefings emphasize CRM and crew performance? We discuss the implications of our findings for ALP and LOE.

Dismukes, Key↗
NASA NTRS2012

Classification of ASKAP Vast Radio Light Curves

The VAST survey is a wide-field survey that observes with unprecedented instrument sensitivity (0.5 mJy or lower) and repeat cadence (a goal of 5 seconds) that will enable novel scientific discoveries related to known and unknown classes of radio transients and variables. Given the unprecedented observing characteristics of VAST, it is important to estimate source classification performance, and determine best practices prior to the launch of ASKAP's BETA in 2012. The goal of this study is to identify light curve characterization and classification algorithms that are best suited for archival VAST light curve classification. We perform our experiments on light curve simulations of eight source types and achieve best case performance of approximately 90% accuracy. We note that classification performance is most influenced by light curve characterization rather than classifier algorithm.

radio astronomy↗
NASA NTRS2020

Statistical Analysis of Factors Riving Surface Ozone Variability over Continental South Africa

Statistical relationships between surface ozone (O3) concentration, precursor species and meteorological conditions in continental South Africa were examined from data obtained from measurement stations in north-eastern South Africa. Three multivariate statistical methods were applied in the investigation, i.e. multiple linear regression (MLR), principal component analysis (PCA) and –regression (PCR), and generalised additive model (GAM) analysis. The daily maximum 8-h moving average O3 concentrations were considered in these statistical models (dependent variable). MLR models indicated that meteorology and precursor species concentrations are able to explain ~50% of the variability in daily maximum O3 levels. MLR analysis revealed that atmospheric carbon monoxide (CO), temperature and relative humidity were the strongest factors affecting the daily O3 variability. In summer, daily O3 variances were mostly associated with relative humidity, while winter O3 levels were mostly linked to temperature and CO. PCA indicated that CO, temperature and relative humidity were not strongly collinear. GAM also identified CO, temperature and relative humidity as the strongest factors affecting the daily variation of O3. Partial residual plots found that temperature, radiation and nitrogen oxides most likely have a non-linear relationship with O3,while the relationship with relative humidity and CO is probably linear. An inter-comparison between O3 levels modelled with the three statistical models compared to measured O3 concentrations showed that the GAM model offered a slight improvement over the MLR model. These findings emphasise the critical role of regional-scale O3 precursors coupled with meteorological conditions in daily variances of O3 levels in continental South Africa.

multiple linear regression (MLR)↗
DOE OSTI2022

When physics-informed data analytics outperforms black-box machine learning: A case study in thickness control for additive manufacturing

Aerosol jet printing (AJP) has emerged as a promising noncontact additive manufacturing method for high-resolution printing for a wide range of material systems. A key challenge limiting the broader adoption of AJP in the material science community is the lack of methods to precisely control thickness. Herein, we develop a model-based design of experiment (MBDoE) framework that integrates physics-informed models, nonlinear regression, and information criteria to postulate, select and calibrate the best model to describe and optimize the AJP manufacturing process. Starting with already available data from system commissioning (e.g., prior single variable sensitivity analysis), four candidate physics-informed models are postulated and trained. MBDoE identifies a single additional optimal experiment to validate these predictive models with quantified uncertainties, which are then used to determine the best experimental conditions to control printed film thickness. As a comparative benchmark, the analysis is repeated using the same dataset with nonparametric Gaussian process regression (GPR) model that does not incorporate physical information. Using MBDoE principles, we find that only five experiments are necessary to calibrate the nonlinear physics-informed parametric model, and with said limited data, this model outperforms the black-box machine learning GPR model. This key result underscores an emerging trend in the data science community: incorporating physical information into predictive models often drastically reduces the data requirements. Leveraging MBDoE further increased the data efficiency. By design, the proposed data science framework is general in nature and can be easily extended to other experimental and additive manufacturing systems beyond AJP.

Aerosol jet printing↗
DOE OSTI2020

Process design tools and techno-economic analysis for capacitive deionization

Capacitive deionization (CDI) devices use cyclical electrosorption on porous electrode surfaces to achieve water desalination. Process modeling and design of CDI systems requires accurate treatment of the coupling among input electrical forcing, input flow rates, and system responses including salt removal dynamics, water recovery, energy storage, and dissipation. Techno-economic analyses of CDI further require a method to calculate and compare between a produced commodity (e.g. desalted water) versus capital and operational costs of the system. In this work, we demonstrate a new modeling and analysis tool for CDI developed as an installable Matlab program that allows direct numerical simulation of CDI dynamics and calculation of key performance and cost parameters. The program is provided for free and is used to run open-source Simulink models. The Simulink environment sends information to the program and allows for a drag and drop design space where users can connect CDI cells to relevant periphery blocks such as grid energy, battery, solar panel, waste disposal, and maintenance/labor cost streams. The program allows for simulation of arbitrary current forcing and arbitrary flow rate forcing of one or more CDI cells. We employ validated well-mixed reactor formulations together with a non-linear circuit model formulation that can accommodate a variety of electric double layer sub-models (e.g. for charge efficiency). The program includes a graphical user interface (GUI) to specify CDI plant parameters, specify operating conditions, run individual tests or parameter batch-mode simulations, and plot relevant results. The techno-economic models convert among dimensional streams of species (e.g. feed, desalted water, and brine), energy, and cost and enable a variety of economic estimates including levelized water costs.

42 ENGINEERING↗
DOE OSTI2022

In Situ Visualization of Local Distortions in the High- T c Molecule-Intercalated Li x (C 5 H 5 N) y Fe 2– z Se 2 Superconductor

A time-resolved synchrotron X-ray total scattering study sheds light on the evolution of the different structural length scales involved during the intercalation of the layered iron–selenide host by organic molecular donors, aiming at the formation of the expanded-lattice Li x (C 5 H 5 N) y Fe 2– z Se 2 hybrid superconductor. The intercalates are found to crystallize in the tetragonal ThCr 2 Si 2 -type structure at the average level, however, with an enhanced interlayer iron–selenide spacing ( d = 16.2 Å) that accommodates the heterocyclic molecular spacers. Quantitative atomic pair distribution function (PDF) analysis at variable times suggests distorted FeSe 4 tetrahedral local environments that appear swollen with respect to those in the parent β-FeSe. Simultaneously acquired in situ synchrotron X-ray powder diffraction data disclose that secondary phases (α-Fe and Li 2 Se) grow significantly when a higher lithium concentration is used in the solvothermal reaction or when the solution is aged. These observations are in line with the strongly reducing character of the intercalation medium’s solvated electrons that mediate the defect chemistry of the expanded-lattice superconductor. In the latter, intralayer correlated local distortions indicate electron-donating aspects that reflect in somewhat enlarged Fe–Se bonds. Additionally, they also reveal a degree of relief of chemical pressure associated with a large distance between Fe and Se sheets (“taller” anion height) and a stretched Fe–Fe square planar topology. The elongation of the latter, derived from the in situ PDF study, speaks for a plausible increase in the Fe-site vacancy concentration. The evolution of the local structural parameters suggests an optimum reaction window where kinetically stabilized phases resemble the distortions of the edge-sharing Fe–Se tetrahedra, required for a high- T c in expanded-lattice iron-chalcogenides.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Thermal Activation of a Copper-Loaded Covalent Organic Framework for Near-Ambient Temperature Hydrogen Storage and Delivery

Copper(II) formate is efficiently incorporated into the pores of a 2D imine-based covalent organic framework (COF) via coordination with the phenol and imine groups. The coordinated metal ion is then reduced to Cu(I) with a thermal treatment that evolves CO 2 . After loading with hydrogen gas, the majority of H 2 desorbs from the coordinatively saturated Cu(II) COF at temperatures < -100 degrees C. However, the activated Cu(I) COF retains adsorbed H 2 above room temperature. Adsorption/desorption of H 2 was highly reversible. Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) strongly supports a molecular hydrogen interaction with Cu(I). A Kissinger analysis of variable ramp rate desorption experiments estimates the enthalpy of H 2 desorption from Cu(I) at 15 kJ mol -1 . The results represent an advance toward practical H 2 storage and delivery in a lightweight, stable, and highly versatile material.

08 HYDROGEN↗
DOE OSTI2023

Train, Inform, Borrow, or Combine? Approaches to Process–Guided Deep Learning for Groundwater–Influenced Stream Temperature Prediction

Although groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously-described process-guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater-relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater-dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temperature and Water Levels Collectively Regulate Methane Emissions From Subtropical Freshwater Wetlands

Abstract Wetlands are the largest and most climate‐sensitive natural sources of methane. Accurately estimating wetland methane emissions involves reconciling inversion (“top‐down”) and process‐based (“bottom‐up”) models within the global methane budget. However, estimates from these two model types are inherently interdependent and often reveal substantial discrepancies. To enhance the reliability of both approaches, we need a comprehensive understanding of wetland methane emissions and an independent high‐resolution long‐term flux data set. Here, we employed a data‐driven random forest approach to identify key variables influencing methane emissions from subtropical freshwater wetlands in the Southeastern United States. The model‐estimated monthly mean methane fluxes fit well with measured methane fluxes ( R 2 = 0.67) at four representative FLUXNET‐CH4 wetland sites across the region. Variable importance analysis highlighted the sensitivity of subtropical freshwater wetland methane emissions to variations in both temperature and water levels. High temperatures facilitate methanogenesis by enhancing microbial activities, while elevated water levels maintain anaerobic conditions necessary for methane production. Notably, the response of methane emissions to water level fluctuations is contingent on temperature conditions, and vice versa. Moreover, we constructed the first high‐spatial‐resolution (∼1 km × 1 km) and long‐term (1982–2010) gridded regional wetland methane flux product for the Southeastern United States, estimating annual methane emissions from subtropical freshwater wetlands in the region at 4.93 ± 0.11 Tg CH 4 yr −1 for 1982–2010. This new benchmark product holds promise for validating and parameterizing uncertain wetland methane emission processes in bottom‐up models and provides improved prior information for top‐down models.

He, Keqi [Earth and Climate Sciences Nicholas Scho↗
DOE OSTI2024

Land Processes Can Substantially Impact the Mean Climate State

Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗
DOE OSTI2024

Rigidochromism of tetranuclear Cu( I )–pyrazolate macrocycles: steric crowding with trifluoromethyl groups

Macrocyclic Cu(I)–pyrazolate tetramers (Cu 4 pz 4 ) can fold into compact structures with luminescent Cu 4 cores whose emission wavelengths are sensitive to steric effects along the periphery of the macrocycle. Introducing CF 3 at the C 4 position of 3,5-di- t Bu-pyrazolate increases steric crowding that modifies the conformational behavior of the Cu 4 pz 4 complex, highlighted by a low-temperature martensitic transition. Variable-temperature analysis of solid-state luminescence reveal an unexpected blueshifting of emission with rising temperature.

Rajagopal, Shinaj K.↗
DOE OSTI2024

Further adoption of conservation tillage can increase maize yields in the western US Corn Belt

Conservation tillage can reduce soil erosion, increase soil health, and decrease labor and fuel input costs. Despite these benefits, potential yield impacts remain an important concern for farmers considering adoption. Previous research suggests that conservation tillage is likely to have the largest yield benefits in more arid conditions, but a lack of field-level analyses across climatic, management and soil conditions limits confidence in such predictions. Satellite imagery provides the opportunity to monitor agricultural lands at sub-field resolution across large spatial scales and wide environmental gradients. Here we investigate the maize yield impacts of conservation tillage in the semi-arid western US Corn Belt, using sub-field resolution datasets on tillage practices and crop yields derived from satellite data spanning four states (Nebraska, Kansas, South Dakota, and North Dakota) between 2008 and 2020. On these datasets, we estimate heterogenous yield outcomes for several thousand maize fields across gradients in climate, soil quality and irrigation status by using a causal forests analysis, an adaptation of the random forests machine-learning algorithm for causal inference on observational data. We find that long-term adoption of conservation tillage increased rainfed maize yields by an average of 9.9% in the region. Impacts on irrigated yields were small and not statistically significant. These results, along with an analysis of variables related to greater than average yield benefits, indicate that improved water infiltration and retention are the primary reasons for conservation tillage benefits. Despite yield benefits, many fields estimated to see increased yields under long term low till have not adopted the practice. Therefore, we identify specific counties likely to benefit most from increased levels of adoption. Our results strengthen the understanding of the impacts of conservation agriculture on crop yields and help define environments and counties most likely to benefit from conservation tillage.

54 ENVIRONMENTAL SCIENCES↗