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Abstract ObjectiveAgricultural productivity is influenced by climate, soil, management, and technology, yet the role of field geometry, including field size, shape, and edge effects, remains poorly understood. This study investigates how geometric field properties relate to crop yield and within-field yield variability in corn, soybean, and wheat, and evaluates the potential effects of edge management through simulated buffering. MethodsMore than twenty years of combine harvester yield monitor data from the US Midwest were analysed. Starting from 18,529 raw yield maps, rigorous geometric cleaning and georeferencing corrections produced a high-quality dataset of 7,207 fields. Mean yield and within-field yield variability were related to field area, perimeter, perimeter-to-area ratio, and compactness, both individually and in combination. In addition, inward buffering of 10 m and 30 m was simulated to quantify the effects of edge removal on field productivity and yield variability. ResultsField geometry was not a dominant driver of agricultural productivity, although consistent patterns emerged across the dataset. Larger and more compact fields tended to exhibit slightly higher yields and lower within-field variability, whereas smaller and more irregular fields performed less favourably. Simulated buffering consistently increased whole-field mean yields, particularly in small fields with high perimeter-to-area ratios, while also producing modest reductions in within-field yield variability. ConclusionAlthough the influence of field geometry on crop productivity is generally modest, edge effects represent a measurable source of yield reduction and spatial variability. The findings suggest that geometry-aware management, including the introduction of ecological buffer zones, could improve both agricultural productivity and environmental sustainability with limited loss of productive land. 

Abstract No-till farming can improve soil health and reduce production costs, yet national adoption remains low, in part because producers face uncertainty about its long-term economic returns. Using more than 30 years of plot-level data from field experiments in Ohio and Michigan, we estimate site-specific effects of no-till on yields and input costs and calculate net profitability using partial budgeting. Results show that no-till consistently reduces operational costs across all sites. In Michigan, no-till also increases yields by 15.6 bu/ac for corn and 5.9 bu/ac for soybean. Estimated net return gains range from approximately $34 to $93 per acre for corn and $30 to $86 per acre for soybean. These results indicate that continuous no-till can generate positive long-run economic returns under the experimental conditions studied, although the magnitude varies across sites and crops. 

Abstract While educators strive to foster inclusive learning environments, a commonly reported concern is the prospect of student resistance to inclusive course content. To investigate the extent of student resistance to inclusive activities, we conducted a post-hoc analysis of more than 6000 student responses to survey questionnaires from a nationwide study that evaluated data literacy activities implemented in undergraduate biology courses that featured scientist role models with counter-stereotypical identities. We found few instances of active student resistance to these inclusive activities. Most student responses (94%) displayed no clear resistance to the activities. Of the resistant responses, only 16 (0.25%) were clearly resistant to the inclusive content within the data literacy activities. This study is the first to attempt to quantify student resistance to inclusive activities featuring counter-stereotypical scientist role models and serves as a springboard for future exploration of student resistance. 

Abstract Plants must respond to changing climatic conditions while continuing to defend against herbivores. While numerous studies have investigated how one type of stress affects plants, the effects of multiple abiotic and biotic stressors and their interactions are less understood. We used the Rainfall Exclusion eXperiment (REX) at the Kellogg Biological Station Long-Term Ecological Research site (KBS LTER) to quantify the individual and interactive effects of drought and warming treatments (abiotic stressors), and galling byRhopalomyia solidaginis(biotic stress) on tall goldenrod (Solidago altissima), a common native plant species in Michigan, USA. At the end of the 2021 and 2022 growing seasons, we measured stem height and biomass as a proxy for plant productivity, and seed mass per stem as a proxy for reproductive fitness. We also measured gall biomass, larval chamber number, and larval chamber volume to reflect the effects of drought and warming on the gallmaker. We found that warming mitigated some negative galling effects; galled plants were 7.1 cm shorter than non-galled plants in ambient conditions, but under warming, there was no reduction in height for galled plants. Furthermore, drought exacerbated some galling effects: galled plants experiencing drought conditions had the lowest probability of producing seeds (0.47) compared to plants from all other treatments. Understanding how plants respond to individual abiotic and biotic stressors as well as their interactions will enhance our ability to predict plant fitness and community dynamics under new climate regimes. 

Abstract Identifying the factors mediating the resilience and invariability of plant communities and their associated ecosystem functions is critical to understand the ecological impacts of climate change. Prior work has explored how classic “community properties” (characterizing a community by species composition) regulate the resilience and invariability of ecosystem functioning. Mechanistically, community properties influence resilience and invariability via species' traits, and as a result, “functional properties” (characterizing a community via functional traits) might better predict these qualities. For example, functional traits associated with conservative resource‐use strategies (e.g., short stature, low specific leaf area [SLA], high leaf dry matter content [LDMC]) are expected to promote both resistance to periods of stress or resource limitation and long‐term invariability. While there is a strong conceptual basis linking functional traits and functional diversity to resilience and invariability, empirical evidence is thus far mixed, and sourcing accurate functional trait data may be challenging. Therefore, it is important to know if community properties are sufficient for evaluating the resilience and invariability of ecosystem functioning, or if functional properties provide necessary insights. Capitalizing on a decades‐long study, we tested the effects of plant functional and community properties on the resistance of ecosystem functioning (a component of resilience) to drought or long‐term invariability. Including functional properties did not improve our ability to explain primary productivity resistance to droughts but considerably improved our explanatory power for productivity invariability. Our results supported expectations that conservative trait strategies promote ecosystem functioning resistance to perturbations and temporal invariability. These findings highlight that the utility of functional properties in explaining the resilience and invariability of ecosystem functioning may depend on the attribute under consideration and that functional properties may primarily be useful for identifying the traits promoting resilience and invariability. 

Abstract Altered surface albedo due to land cover conversions and management is a significant driver of global climate change. Albedo can be directly measured at ground stations, and remote sensing data can be used to scale-up albedo values to regional and global levels. Some previous studies have retrieved fine resolution (10–30 m) instantaneous albedo and coarse resolution (500–1000 m) daily mean albedo from remote sensing data, but they all required the input of MODIS albedo information at 500 m resolution, and none have assembled both instantaneous and daily albedo based exclusively on fine resolution satellite data. To address this issue, we compiled 387 instantaneous and 346 daily albedo records using field net radiometer measurements from the bioenergy croplands at the W. K. Kellogg Biological Station in southwest Michigan. We then connected these albedo records with a suite of variables derived from Harmonized Landsat and Sentinel-2 data through two machine learning algorithms (random forest regression and extreme gradient boosting) to retrieve clear-sky instantaneous and daily shortwave broadband albedo. The performance statistics indicate reasonable accuracy of model results (RMSE around or below 0.03 except for snow-covered surfaces), suggesting that the retrieval of both instantaneous and daily albedo based exclusively on fine resolution satellite data is promising. To facilitate the use of fine resolution albedo products at the global level, future efforts need to include more albedo records of diverse surface cover types, as well as to accurately model daily albedo for cloudy days to address the “clear-sky bias”. 

Abstract Soil organic carbon (SOC) models need independent evaluation against field measurements, but those latter are rarely publicly available and harmonized. In this study, we collected and shared data from 167 agronomic treatments in 34 agronomic long-term experiments (LTEs) located in temperate croplands, allowing the evaluation of several soil organic C models such as RothC, Century, AMG, MIMICS, ICBM, Millenial, and CTOOL. The dataset includes climate data, soil properties, C inputs from crops (n = 4588 records) and organic amendments, irrigation data, monthly soil cover, as well as SOC stock measurements in the topsoil layer (n = 1328 records). Climate, soil moisture, and soil temperature data were extracted from daily climate databases. Carbon inputs from crops were calculated from observed yields and harvest index, with some harvest index values estimated, combined with crop allometric coefficients from the literature. Descriptions of LTE, agronomic treatments, methodological metadata, and a part of the code, accompanies the dataset. The dataset can be reused to evaluate single SOC models, or to evaluate an ensemble of models. 

Abstract Combining regenerative agriculture (RA) practices, such as cover cropping and no‐till, is increasingly promoted as a strategy to enhance soil health and climate resilience. However, their long‐term impacts on crop yield and yield stability remain uncertain. In this study, we assessed how RA practices influence sub‐field spatial variability of crop yield. We use yield stability zone categories (high, medium, low, or unstable) across 10 commercial farm fields in Michigan over a long period of time, 6–9 years before RA were implemented and 9–10 years after RA were implemented. We found that RA practices did not statistically impact maize (Zea maysL.; +9%) or soybean (Glycine max(L.) Merr.; –3%) yields, but RA increased whole field yield stability (decrease of CV from 27% to 17%) and the proportion of high‐yielding zones within the fields (+28%) due to the conversion of a substantial portion (21%) of prior unstable yielding areas of the field. Our results suggest that combining RA practices, such as no‐till and cover crops, can improve crop productivity and yield stability through the conversion of unstable zones into more stable areas of crop production, benefiting long‐term soil health and broader environmental goals in the Upper US Midwest. 

ABSTRACT Ecosystem resistance and resilience to extreme climate events is impacted by community properties, including biodiversity. However, the relative importance of species richness, evenness and dominance is debated and is further modulated by global change factors such as nutrient addition. Using nearly 40 years of data from naturally‐assembled plant communities at three Long‐Term Ecological Research sites, we found that while species richness is important for resistance to extreme dry events, dominance is important for resistance to extreme wet events and evenness is important for resilience under ambient (unfertilized) conditions. Furthermore, nutrient addition alters resistance and resilience indirectly by reducing species richness and increasing dominance. Species richness and dominance are also directly reduced by extreme climate events, which may erode resistance and resilience to future events. Our results show that species richness, dominance and evenness shape ecosystem stability under climate extremes and that fertilization fundamentally modifies biodiversity–stability relationships in mesic grasslands. 

Abstract Nitrogen (N) supply from cover crops to subsequent crop primarily depends on cover crop biomass production. Questions remain on how cover crop biomass interacts with abiotic factors to affect soil inorganic N and when N availability is highest following cover crop termination. This study identified key variables influencing N mineralization dynamics in cover crop systems, including air temperature, precipitation, gravimetric water content, cover crop biomass, weed biomass, and days after cover crop termination (DAT). Using random forest modeling with leave‐one‐year‐out cross‐validation, we analyzed 30 years (1990–2020) of bi‐weekly soil N measurements in two corn (Zea maysL.)‐soybean (Glycine max)‐wheat (Triticum aestivumL.) rotations with a legume cover crop (Trifolium pratenseL.) to identify key drivers of soil inorganic N release. Models explained 35% of variability in soil NO₃⁻‐N and 15%–32% variability in soil NH₄⁺‐N in the two systems. Variable importance analysis revealed that DAT was the most important driver affecting soil inorganic N availability, with air temperature as a close second. Partial dependence plots showed that soil NO₃⁻‐N increased rapidly following cover crop termination and peaked at approximately 50 DAT. Two‐dimensional partial dependence plots revealed interactions among DAT, temperature, and cover crop biomass in affecting soil NO₃⁻‐N. Temperature >12°C and cover crop biomass above 4000 kg ha⁻¹were associated with high soil NO₃⁻‐N levels. There were productivity differences between the management systems studied, yet both systems showed similar N dynamics, suggesting this approach was robust for understanding underlying drivers concerning N mineralization.