Accurately estimating crop yields across scales (e.g., regional administrative and field scales) is critical for global food security, given mounting challenges agriculture faces from climate change, population growth, soil degradation, and water scarcity. Remote sensing has been widely used for crop yield estimation with its spatially extensive coverage and temporally continuous observations of crop conditions. However, robust yield estimation across scales remains challenging, particularly under conditions of limited availability of crop yield and crop management data. While process-based crop models can capture complex interactions of plant-soil-atmosphere continuum, their large-scale application is limited by computational cost, parameterization difficulty, and model input (e.g., management) constraints. Empirical data-driven models are more flexible and computationally efficient, but they heavily depend on ground-measured yield labels that are often scarce over space and time. To address these challenges, we propose PHYIELD, a novel hybrid yield estimation framework that integrates systematically calibrated process-based crop modeling and phenology-guided deep learning for maize yield estimation at both county and field scales under yield data constraint conditions. PHYIELD mainly comprises three components: systematic crop model calibration, diversified crop model simulation, and phenology-guided deep learning. Given the challenges and prevalence of sparse yield observations in practice, PHYIELD is uniquely devised with limited ground yield records for model building. Its model performance is systematically evaluated for temporal, spatial, and scale transfers, upon comparison with standalone crop model and deep learning counterpart benchmarks. Results showed that the systematic crop model calibration of PHYIELD, particularly via the calibration for Leaf Area Index (LAI), followed by yield (biomass multiplied by Harvest Index [HI]), is essential for region-varying parameterizations of the crop model before coupling with deep learning. With diversified simulations from calibrated crop model and phenology-guided deep learning, PHYIELD achieved correlation (R) of 0.53–0.73, bias of −1.47–0.96 t ha⁻1, and root mean square error (RMSE) of 1.07–1.68 t ha⁻1 in the study region, outperforming both crop model and deep learning benchmarks. Under significant yield data constraint condition (e.g., only 2023 county-level yield data of Illinois available), PHYIELD demonstrated superior temporal transferability over the preceding 20 year period (2003–2022) (R = 0.70, RMSE = 1.76 t ha⁻1), spatial transfer to surrounding states (R = 0.85, RMSE = 1.06 t ha⁻1), and scale transfer from county to field levels (R = 0.53–0.76, RMSE = 1.55–1.58 t ha⁻1). By leveraging the complementary strengths of process-based and data-driven modeling approaches, coupled with systematic parameterization of crop model and incorporation of phenology knowledge, PHYIELD provides a more robust and generalizable solution for multi-scale maize yield estimation, particularly under limited data conditions. It can largely support yield-oriented decision-making in agricultural management, policy development, and food security planning.
Acoustic features of child directed speech (CDS) in caregiver-child conversational interactions are known to capture young children’s attention to language input and facilitate language learning. Emerging evidence suggests that caregivers’ use of CDS-like features when reading to their child, known as caregiver oral reading prosody, plays an important role in supporting preschoolers’ storybook comprehension. Do associations between caregiver oral reading prosody and preschoolers’ language extend beyond real-time listening comprehension to broader language skills? Forty-one caregivers read a children’s book to their child (ages: 4–5 years) as they normally would at home. The caregivers’ oral reading prosody (i.e., intonation and timing features) was examined in relation to children’s receptive vocabulary and core language skills. Moderated mediation models then investigated possible impacts of caregiver reading skills and shared reading time on these associations. Caregiver intonation range positively related to child vocabulary and caregiver inappropriate (i.e., ungrammatical) pausing negatively related to child core language skills. Mediation models revealed that caregiver reading skills indirectly related to child language through oral reading prosody measures. Notably, the indirect effect linking caregiver reading skills to child vocabulary via caregiver intonation range was moderated by shared reading time, with the strongest effects among dyads reporting less shared reading engagement. Findings illuminate caregiver oral reading prosody as one factor linking caregiver reading skills to child language, with particular relevance among families of caregivers with reading difficulties and for those with limited shared reading opportunities.
Decarbonization of the residential built environment requires widespread adoption of social practices that improve energy efficiency, support electrification, and integrate renewable energy. Real estate agents can influence how decarbonization interventions are interpreted at open houses and at the point-of-sale, especially given they must frame the homes they sell for the imagined lifestyle demands of future occupants, yet their role in shaping buyer-facing narratives is unclear. Drawing on qualitative data from “mystery shopper” participant-observer visits to 60 open houses in United States cities Boston and Phoenix, this study investigates how real-estate agents frame residential sustainability amenities to potential buyers. We find that agents rarely volunteer information about sustainability features unless prompted by interested buyers. When prompted, agents offered ambivalence about solar technologies, particularly leased systems, but mobilize more favorable frameworks for other amenities, including electric vehicle charging capability and high-quality insulation. Applying Shove’s account of resource-intensive practices organized around “cleanliness, comfort, and convenience,” we show that, when discussed, sustainability amenities were most often favorably communicated when supporting convenience, followed by comfort and protection. These findings highlight real estate agents as overlooked intermediaries in residential decarbonization and suggest that point-of-sale communication may shape whether sustainability features in residential exchange are recognized as desirable or risky.
Industrial decarbonization is rapidly transforming employment structures and regional economies in major carbon emitting countries. Yet both conceptual foundations and empirical work on energy justice remain geographically concentrated in WEIRD (Western, educated, industrialized, rich, and democratic) societies. Existing research leaves limited insight into how the Chinese public understands and responds to justice concerns arising during decarbonization. Drawing on a mixed-methods design that combines an original cross-sectional online quota survey (N = 1,042) with 52 semi-structured expert interviews conducted across China, we examine how distributive, procedural, recognition, and cosmopolitan justice are reflected in respondents’ views on industrial decarbonization and analyze the institutional logics underlying these perceptions. The findings show that these views center on visible policy benefits, livelihood-related costs, and responsibility allocation. Procedural concerns extend beyond participation to transparent implementation, reliable enforcement, differentiated policy application, and information that supports oversight. Recognition justice is shaped by the visibility of different forms of vulnerability, while cosmopolitan justice is interpreted through competing narratives of historical responsibility, the right to development, South-South cooperation, and external pressure. These findings suggest that institutional context shapes how respondents translate abstract justice principles into judgments about policy goals, implementation approaches, responsible actors, and affected groups. For policy, the findings point to the need to clarify government and industry responsibilities for transition costs and limit the transfer of these costs to consumers. Public participation may be most meaningful when it informs compensation and support for affected groups.
Entrainment is a social adaptive mechanism which in human spoken interaction includes interlocutors unconsciously adjusting their vocal patterns and related behaviors to match those of their conversation partner. Entrainment offers important insights toward understanding the socio-cognitive characteristics of an individual. Quantifying entrainment patterns can also inform clinical diagnosis, long-term monitoring, and individualized interventions in neuro-developmental disorders characterized by deficits in communication and social interaction, such as Autism Spectrum Disorder (ASD). In this work, we model vocal entrainment in dyadic child-inclusive conversations to analyze behavioral traits of children with and without an autism diagnosis. Specifically, we explore contrastive-learning based unsupervised modeling to learn representations related to entrainment from speech features. We validate the proposed measures by using them to differentiate real conversations from simulated shuffled ones. Furthermore, we illustrate their utility in modeling various behaviors relevant to autism symptoms by correlation experiments and comparing the variation of the introduced measures in children under different demographic conditions.
