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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Enhanced Photo-Cross-Linking of Thymines in DNA Holliday Junction-Templated Squaraine Dimers

Programmable self-assembly of dyes using DNA templates to promote exciton delocalization in dye aggregates is gaining considerable interest. New methods to improve the rigidity of the DNA scaffold and thus the stability of the molecular dye aggregates to encourage exciton delocalization are desired. In these dye–DNA constructs, one potential way to increase the stability of the aggregates is to create an additional covalent bond via photo-cross-linking reactions between thymines in the DNA scaffold. Specifically, we report an approach to increase the yield of photo-cross-linking reaction between thymines in the core of a DNA Holliday junction while limiting the damage from UV irradiation to DNA. We investigated the effect of the distance between thymines on the photo-cross-linking reaction yields by using linkers with different lengths to tether the dyes to the DNA templates. By comprehensively evaluating the photo-cross-linking reaction yields of dye–DNA aggregates using linkers with different lengths, we conclude that interstrand thymines tend to photo-cross-link more efficiently with short linkers. A higher cross-linking yield was achieved due to the shorter intermolecular distance between thymines influenced by strong dye–dye interactions. Furthermore, our method establishes the possibility of improving the stability of DNA-scaffolded dye aggregates, thereby expanding their use in exciton-based applications such as light harvesting, nanoscale computing, quantum computing, and optoelectronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Nanostructuring and Underscreening of Bisalt Electrolytes with Dual-Anion Effects: Insights from Small-Angle Scattering Prepeak Analysis

Using small-angle neutron scattering, we studied the nanostructure of a prototypical D(H)-bonded network electrolyte, alkaline sodium aluminate bisalt, at concentrations up to solute volume fraction of ~0.5. Analysis of the structure factor prepeak at 0.1 < Q < 1.0 Å-1 showed that its evolution is associated with nanoscopic species distribution in water-anion network., with differences in characteristic distance d between OD- and Al(OD)4- anions related to their distinct sizes and interactions. When the solute volume fraction approached 0.5, a common/maximum value of ~4.5 Å was found for the correlation length ? that characterizes the electrostatic force in concentrated electrolyte solution before precipitation occurred. This implies that the solutions’ morphology and behavior at high concentrations may be governed by geometric factors, rather than the chemistry of the specific anion. Furthermore, the scaling of ? with concentration yielded an exponent of 1.25(1), suggesting that ? is limited to a few Å. By combining the current observations of structural heterogeneity at the nanoscale, with dynamic heterogeneity at the microscopic scale from our previous quasi-elastic neutron scattering study, we have established a structural origin of local “caging” and restricted structural relaxation processes. These local solvent-solute interactions not only control dynamics heterogeneity in concentrated electrolytes but also are responsible for crystallization processes in industrial setting, such as aluminum production and radioactive waste treatment.

Wang, Hsiu-Wen↗
DOE OSTI2023

Analysis of Needlet Internal Linear Combination performance on B -mode data from sub-orbital experiments

The observation of primordial B modes in cosmic microwave background (CMB) polarisation data represents the main scientific goal of most of the future CMB experiments. This signal is predicted to be much lower than polarised Galactic emission (foregrounds) in any region of the sky, pointing to the need for effective component separation methods. Aims. Among all the techniques, the blind Needlet Internal Linear Combination (NILC) is of great relevance given our current limited knowledge of the B-mode foregrounds. In this work, we explore the possibility of employing NILC for the analysis of B modes reconstructed from partial-sky data, specifically addressing the complications that such an application yields such as E–B leakage, needlet filtering, and beam convolution. We consider two complementary simulated datasets of future experiments: the balloon-borne Short Wavelength Instrument for the Polarisation Explorer (SWIPE) of the Large Scale Polarisation Explorer, which targets the observation of both reionisation and recombination peaks of the primordial CMB B-mode angular power spectrum, and the ground-based Small Aperture Telescope of Simons Observatory, which, instead, is designed to observe only the recombination bump at ℓ ~ 80. We assessed the performance of the following two alternative techniques to correct for the CMB E–B leakage: the recycling technique and the Zhao-Baskaran method. We find that both techniques reduce the E–B leakage residuals at a negligible level given the sensitivity of the considered experiments, except for the recycling method in the SWIPE footprint at ℓ < 20. Thus, we implemented two extensions of the pipeline, the iterative B decomposition and the diffusive inpainting, which enabled us to recover the input CMB B-mode power for ℓ ≥ 5. For the considered experiments, we demonstrate that needlet filtering and beam convolution do not affect the CMB B-mode reconstruction. Finally, with an appropriate masking strategy, we find that NILC foregrounds subtraction allows one to achieve sensitivities on the tensor-to-scalar ratio in agreement with the targets of the considered CMB experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Statistical and systematic uncertainties in extracting the source properties of neutron star-black hole binaries with gravitational waves

Gravitational waves emitted by neutron star black hole mergers encode key properties of neutron stars - such as their size, maximum mass and spins - and black holes. However, the presence of matter and the high mass ratio makes generating long and accurate waveforms from these systems hard to do with numerical relativity, and not much is known about systematic uncertainties due to waveform modeling. In this work, we simulate gravitational waves from neutron star black hole mergers by hybridizing numerical relativity waveforms produced with the SpEC code with a recent numerical relativity surrogate NRHybSur3dq8Tidal. These signals are analyzed using a range of available waveform families, and statistical and systematic errors are reported. We find that at a network signal-to-noise ratio (SNR) of 30, statistical uncertainties are usually larger than systematic offsets, while at an SNR of 70 the two become comparable. The individual black hole and neutron star masses, as well as the mass ratios, are typically measured very precisely, though not always accurately at high SNR. At a SNR of 30 the neutron star tidal deformability can only be bound from above, while for louder sources it can be measured and constrained away from zero. All neutron stars in our simulations are non-spinning, but in no case we can constrain the neutron star spin to be smaller than ~0.4 (90% credible interval). Waveform families whose late inspiral has been tuned specifically for neutron star black hole signals typically yield the most accurate characterization of the source parameters. Their measurements are in tension with those obtained using waveform families tuned against binary neutron stars, even for mass ratios that could be relevant for both binary neutron stars and neutron star black holes mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Assessment of Bulk Oxygen Capacity and Transient Redox Behavior of Foamed Lanthanum Strontium Manganese Perovskites

Synthesis of solar thermochemical hydrogen (STCH) production redox materials with engineered structures, for example, replica foams, can enable efficient heat and mass transport and are critical for scaled-up systems. Prior work has motivated the use of lanthanum strontium manganese (LSM)-type perovskites as foamed STCH materials, but the effect of their morphology on bulk and kinetic behavior has not been reported. In this work, replica and direct foamed samples of La 0.65 Sr 0.35 MnO 3-δ (LSM35) were fabricated and compared to synthesized powders and dense monoliths, and similarly synthesized CeO 2-δ (ceria) foams, regarding their specific reaction rates and bulk oxygen capacity/H 2 yields. Changes in oxygen capacity (Δδ) and reaction rates were measured between 1200 °C and 1400 °C by using fixed ratios of steam and hydrogen during both reduction and oxidation steps, allowing for analysis under practical high conversion conditions. Results suggest bulk behavior and reaction rates of the foamed LSM materials are comparable to their powder analogues. Differences in reaction rates were observed only when replica foamed samples were subjected to rapid laser heating (emulating conditions expected in solar furnaces), which is expected but has not been demonstrated at such a small scale. Here, foamed samples were further subjected to 50 redox cycles at 1400 °C to evaluate their stability. Results show no statistically significant decrease in hydrogen production for any of the foamed samples, but the direct foamed samples became brittle with time. Together, these results demonstrate the viability of replica foamed LSM perovskites for integration in scaled-up STCH systems.

08 HYDROGEN↗
DOE OSTI2026

Design Trade-Offs in Composite Fuel Cell Membranes: Effects of Reinforcement and Chemical Additives

Perfluorosulfonic acid (PFSA) membranes are critical components in proton exchange membrane fuel cells, where performance depends on balancing ionic conductivity, mechanical durability, and chemical stability. This study characterizes a composite membrane (NC700) featuring PFSA-impregnated expanded polytetrafluoroethylene (ePTFE) reinforcement and cerium-based radical scavengers, benchmarked against unreinforced NR211. Complementary techniques, including electron microscopy, X-ray scattering, infrared spectroscopy, thermogravimetric analysis, and dynamic mechanical analysis, identify the structural and compositional strategies employed in NC700. Water sorption isotherms reveal lower water uptake for NC700 across all conditions, attributed to reinforcement and cerium incorporation. Reinforcement reduces in-plane swelling from 11% to 2.1% at 90% RH, confirming strong swelling anisotropy, while maintaining mechanical properties at elevated temperatures. While the ionic conductivity of NC700 is approximately 10% lower than that of NR211, the reduced thickness yields a 40% decrease in calculated area-specific resistance, suggesting the composite architecture can favorably shift the conductivity-stability trade-off. The composite structure also reduces gas permeability, indicating potential for improved separator function alongside favorable transport properties. Systematic deconvolution of reinforcement and additive contributions shows that conductivity losses from cerium incorporation are largely offset by gains from the lower equivalent-weight polymer, providing quantitative relationships that may guide composite membrane design for fuel cells and other electrochemical applications.

25 ENERGY STORAGE↗
DOE OSTI2021

AlSb as a material for high index contrast nanophotonics

High contrast materials, i.e., materials with a high refractive index and low optical loss, are of wide interest for nanophotonics and metasurface designs at optical and near infrared wavelengths. We explore aluminum antimonide (AlSb) as a high contrast nanophotonic material, using the design of high contrast gratings (HCGs) for low loss dielectric mirrors as an example. The high index of refraction and low absorption coefficient of AlSb in the visible wavelength range enable designs of HCGs that can be effectively optimized to form mirrors with 93.5% reflectivity at red visible wavelengths. We detail a co-sputtering synthesis method for AlSb films, and achieve our target high index of refraction of 3.5 for 635 nm light. We also find that the high sensitivity of AlSb oxidation requires specific handling procedures in developing deposition processes to yield a near zero absorption coefficient.

Bauser, Haley C.↗
DOE OSTI2025

SCGSR Final Report

Dual phase noble liquid time projection chambers (TPCs) have an unprecedented sensitivity towards dark matter searches and neutrinos, owing to their ability to detect single electrons. When a particle deposits energy into one of these detectors, electrons are released and drifted into a gaseous region of high electric field. In the gas, the electrons are accelerated to produce extra light into what is called an S2 pulse. This light is far easier to detect than the current induced from a single electron. The two widely used elements for noble liquid detectors are Xe and Ar. Xe has a larger chance of being hit by neutrinos or dark matter, but ends with a lower (i.e. harder to detect) energy when struck. However, Ar light is hard to reflect and detect due to its short wavelength which is absorbed by most materials. While one may use TPB to re-emit this light at a longer wavelength, spatial variations of TPB thickness can smear the amount of re-emitted light, and thus cause a poor S2 energy resolution and spatial resolution. Furthermore, a single electron pulse in Ar is spread over a much longer time than one in Xe, which makes it harder to identify single electrons in Ar. The goal of Xe doping is to combine the best aspects of Ar with the best aspects of Xe. More specifically, this means a detector with a larger yield of photons and electrons produced per energy deposition, an ability to collect more of the light given by those signals, and a narrower pulse from the electron signal. During this project, we developed and operated a detector that can overcome the thermodynamic challenges of Xe doping – namely, preventing Xe from freezing. In the first experimental run during this award period, we found that we can achieve at least 5% of Xe in LAr, and measure the Xe concentration in the gas. In the second run, we installed our detector and saw the first S2 pulses in a dual phase Xe doped LAr TPC. With the data we took, we quantified the effect of Xe doping on the detectability of wavelength-shifted light produced in a Xe doped Ar mixture, as well as the single electron pulse shape. While the Ar light has successfully been wavelength shifted, we learned that much to our surprise, Xe doping actually makes the S2 pulse wider before it makes it narrower.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Advanced Geomechanical Model to Predict the Impact of CO2-Induced Microstructural Alterations on the Cohesive-Frictional Behavior of Mt. Simon Sandstone

We investigated the influence of CO2-induced geochemical reactions on the cohesive-frictional properties of host rock within the context of CO2 storage in a saline aquifer and focused on the Mt. Simon sandstone. The research objective was to model geo-mechanical changes due to host rock exposure to CO2-saturated brine while accounting for heterogeneity, double-scale porosity, and granular structure. We formulated a three-level multi-scale model for host rocks. We conducted scanning electron microscopy analyses to probe the microstructure and grid nanoindentation to measure the mechanical response. We derived new nonlinear strength upscaling solutions to correlate the effective strength characteristics and the macroscopic yield surface to the micro-structure at the nano-, micro-, and meso-scales. Specifically, our theoretical model links CO2-induced microstructural alterations to a reduction in the size of the yield surface, and a drop in the value of the friction coefficient. In turn, regarding the Illinois Basin Decatur Project, the CO2-induced drop in friction coefficient is linked to an increase in the risk of fault slip and a higher probability of induced microseismicity during and after the end of CO2 underground injection operations. The theoretical model presented is essential for the geo-mechanical modeling of CO2 underground injection operations at multiple length-scales.

58 GEOSCIENCES↗
DOE OSTI2022

High-throughput characterization, correlation, and mapping of leaf photosynthetic and functional traits in the soybean ( Glycine max ) nested association mapping population

We report photosynthesis is a key target to improve crop production in many species including soybean [Glycine max (L.) Merr.]. A challenge is that phenotyping photosynthetic traits by traditional approaches is slow and destructive. There is proof-of-concept for leaf hyperspectral reflectance as a rapid method to model photosynthetic traits. However, the crucial step of demonstrating that hyperspectral approaches can be used to advance understanding of the genetic architecture of photosynthetic traits is untested. To address this challenge, we used full-range (500–2,400 nm) leaf reflectance spectroscopy to build partial least squares regression models to estimate leaf traits, including the rate-limiting processes of photosynthesis, maximum Rubisco carboxylation rate, and maximum electron transport. In total, 11 models were produced from a diverse population of soybean sampled over multiple field seasons to estimate photosynthetic parameters, chlorophyll content, leaf carbon and leaf nitrogen percentage, and specific leaf area (with R 2 from 0.56 to 0.96 and root mean square error approximately <10% of the range of calibration data). We explore the utility of these models by applying them to the soybean nested association mapping population, which showed variability in photosynthetic and leaf traits. Genetic mapping provided insights into the underlying genetic architecture of photosynthetic traits and potential improvement in soybean. Notably, the maximum Rubisco carboxylation rate mapped to a region of chromosome 19 containing genes encoding multiple small subunits of Rubisco. We also mapped the maximum electron transport rate to a region of chromosome 10 containing a fructose 1,6-bisphosphatase gene, encoding an important enzyme in the regeneration of ribulose 1,5-bisphosphate and the sucrose biosynthetic pathway. The estimated rate-limiting steps of photosynthesis were low or negatively correlated with yield suggesting that these traits are not influenced by the same genetic mechanisms and are not limiting yield in the soybean NAM population. Leaf carbon percentage, leaf nitrogen percentage, and specific leaf area showed strong correlations with yield and may be of interest in breeding programs as a proxy for yield. This work is among the first to use hyperspectral reflectance to model and map the genetic architecture of the rate-limiting steps of photosynthesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data for Greenhouse Gas Accounting Procedures in Low Carbon Fuel Policies Overlook the Spatial Variability of Miscanthus-Derived Sustainable Aviation Fuel

Low carbon fuel policies such as the U.S. Renewable Fuel Standard (RFS), Canada Clean Fuel Regulations (CFR), and California Low Carbon Fuel Standard (LCFS) as well as the 45Z tax credit are intended to reduce greenhouse gas (GHG) emissions from transportation. Cellulosic feedstocks, optimized biorefineries, and favorable farming locations can significantly reduce biofuel carbon intensity (CI). Despite advances in field-to-fuel GHG monitoring and flexibility in resource allocation within biorefineries (e.g., governing net electricity production), rigid CI accounting procedures in current policies may limit CI responsiveness across candidate sites and processing facilities. This work examines a hypothetical biomass-to-sustainable aviation fuel (SAF) pathway using miscanthus and alcohol-to-jet (i) to demonstrate how GHG accounting requirements drive estimates of biofuel CIs and (ii) to explore potential CI and financial implications of scenario-specific life cycle assessment (LCA). Results demonstrate that GHG accounting using the CFR/LCFS can reasonably account for distinct levels of net electricity production by a biorefinery, but only the CFR yields similar CI sensitivity to spatially explicit factors (feedstock CI, grid electricity CI) as scenario-specific LCA: most GHG accounting frameworks do not capture CI variation across candidate sites in the United States. Ultimately, this work demonstrates the importance of LCA methodological specifications in low carbon fuel policies and tax credits.

Miscanthus↗
DOE OSTI2021

An adaptive adversarial domain adaptation approach for corn yield prediction

Recently, statistical machine learning and deep learning methods have been widely explored for corn yield prediction. Though successful, machine learning models generated within a specific spatial domain often lose their validity when directly applied to new regions. To address this issue, we designed an unsupervised adaptive domain adversarial neural network (ADANN). Specifically, through domain adversarial training, the ADANN model reduced the impact of domain shift by projecting data from different domains into the same subspace. Also, the ADANN model was designed to be trained in an adaptive way, which guaranteed the model can learn the domain-invariant features and perform accurate yield prediction simultaneously. Informative variables including time-series vegetation indices and sequential weather observations were first collected from multiple data sources and aggregated to the county level. Then, we trained the ADANN model with the extracted features and corresponding reported county-level corn yield from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated in four testing years 2016–2019. The U.S. corn belt was used as the study area and counties under study were grouped into two diverse ecological regions. Overall, the experimental results showed that the developed ADANN model had better performance than three other state-of-the-art machine learning models in both local experiments (train and test in the same region) and transfer experiments (train and test in different regions). As the first study using adversarial learning for crop yield prediction, this research demonstrates a novel solution for improving model transferability on crop yield prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Connecting agriculture and renewable energy: insights into microclimatic changes, physiological, biochemical, and yield responses under agrivoltaics: a review

Agrivoltaics, the synergistic integration of agriculture and solar energy production on the same piece of land, has emerged as a compelling dual-use solution that maximizes land productivity while simultaneously addressing the need for sustainable agricultural practices and renewable energy generation. Despite the growing global interest in this dual-use system, the microclimatic shifts created beneath solar panels and their consequences for crop performance remain insufficiently synthesized. This review highlights the intricate interactions between agrivoltaics systems and plant microclimates, discussing their impacts on various physiological processes, metabolic pathways, and overall yield responses in different crop species. Evidence indicates that moderated light intensity and altered microclimates can enhance water-use efficiency, stabilize photosynthetic function, and trigger beneficial metabolic adjustments; however, responses remain highly species-specific and strongly dependent on regional climate conditions and panel configuration. Yield outcomes vary widely among vegetables, cereals, pulses, and fruit crops, highlighting the necessity for tailored agronomic strategies and crop selection within agrivoltaic designs. A critical knowledge gap identified in this review concerns the limited understanding of molecular and omics-level responses underlying plant adaptation to agrivoltaic environments. We further provide a detailed and interdisciplinary overview of adaptive agronomic strategies, and optimal crop selection, tailored to agrivoltaic systems. Despite the benefits of land use efficiency and simultaneous food and energy production, challenges remain concerning initial investment, technological adaptation, social and legal barriers, and shade-induced yield penalties. Further research in this area will be critical to enhancing the agricultural, environmental, and economic sustainability of agrivoltaics while simultaneously augmenting their practical utility and appeal to farmers in the future.

14 SOLAR ENERGY↗
DOE OSTI2021

Considering uncertainties expands the lower tail of maize yield projections

Crop yields are sensitive to extreme weather events. Improving the understanding of the mechanisms and the drivers of the projection uncertainties can help to improve decisions. Previous studies have provided important insights, but often sample only a small subset of potentially important uncertainties. Here we expand on a previous statistical modeling approach by refining the analyses of two uncertainty sources. Specifically, we assess the effects of uncertainties surrounding crop-yield model parameters and climate forcings on projected crop yield. We focus on maize yield projections in the eastern U.S.in this century. We quantify how considering more uncertainties expands the lower tail of yield projections. We characterized the relative importance of each uncertainty source and show that the uncertainty surrounding yield model parameters is the main driver of yield projection uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Elucidating texture and grain morphology contributions to the micromechanical response of additively manufactured Inconel 625

Microstructural variation of additively manufactured (AM) metal components in comparison to wrought counterparts makes certification for critical applications a challenge. Microscale simulations leveraging modern computational tools may be used to supplement testing of AM microstructures, thus accelerating certification by reducing the number of experiments needed. However, as micromechanical response is closely tied to critical properties like fatigue-life and fracture, utilization of these simulations with macroscale experimental data alone is insufficient. One means to attain microscale experimental data is in situ diffraction data collected from synchrotron X-ray sources. In this work, such data were collected during in situ compression of AM Inconel 625 superalloy. Interpretation of experimental results was assisted by massive (8M element) complementary micromechanical simulations performed on sets of virtual microstructures generated using cellular automata. Together, micromechanical data from diffraction experiments and simulations were used to probe the effects of textured “track” microstructures generated during laser powder bed fusion and directional strength-to-stiffness on micromechanical response. Though fiber-averaged directional strength-to-stiffness ratios were expected to dominate given the high elastic anisotropy of the material, the combination of small variations in texture and specific grain configurations unique to AM microstructures lead to significant variability in micromechanical response after yield. The findings emphasize the importance of high-fidelity microstructural representation that captures key texture components and AM-specific morphology for property prediction of AM metals.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A coarse-grained simulation model for colloidal self-assembly via explicit mobile binders

Colloidal particles with mobile binding molecules constitute a powerful platform for probing the physics of self-assembly. Binding molecules are free to diffuse and rearrange on the surface, giving rise to spontaneous control over the number of droplet–droplet bonds, i.e., valence, as a function of the concentration of binders. This type of valence control has been realized experimentally by tuning the interaction strength between DNA-coated emulsion droplets. Optimizing for valence two yields droplet polymer chains, termed ‘colloidomers’, which have recently been used to probe the physics of folding. To understand the underlying self-assembly mechanisms, here we present a coarse-grained molecular dynamics (CGMD) model to study the self-assembly of this class of systems using explicit representations of mobile binding sites. Further, we explore how valence of assembled structures can be tuned through kinetic control in the strong binding limit. More specifically, we optimize experimental control parameters to obtain the highest yield of long linear colloidomer chains. Subsequently tuning the dynamics of binding and unbinding via a temperature-dependent model allows us to observe a heptamer chain collapse into all possible rigid structures, in good agreement with recent folding experiments. Our CGMD platform and dynamic bonding model (implemented as an open-source custom plugin to HOOMD-Blue) reveal the molecular features governing the binding patch size and valence control, and opens the study of pathways in colloidomer folding. This model can therefore guide programmable design in experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗