Contagious Yawning, Empathy, and Their Relation to Prosocial Behavior

Humans express facial mimicry across a variety of actions. This article explores a distinct example, contagious yawning, and the links to empathy and prosocial behavior. Prior studies have suggested that there is a positive link between empathy and the susceptibility to contagious yawning. However, the existing evidence has been sparse and contradictory. We present results from 2 laboratory studies conducted with 171 (Study 1) and 333 (Study 2) student volunteers. Subjects were video-recorded while watching muted videos of individuals yawning, scratching, or laughing. Empathy was measured using the Interpersonal Reactivity Index. Although subjects imitated all facial expressions to large extents, our studies show that only contagious yawning was related to empathy. Subjects who yawned in response to observing others yawn exhibited higher empathy values by half a standard deviation. However, we found no evidence that the susceptibility to contagious yawning is directly related to prosocial behavior

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied

Predicting Economic Optimal Nitrogen Rate with the Anaerobic Potentially Mineralizable Nitrogen Test

Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of corn (Zea mays L.) economic optimal N rate (EONR). A study across eight US midwestern states was conducted to quantify the predictability of EONR for single and split N applications by PMNan. Treatment factors included different soil sample timings (pre-plant and V5 development stage), planting N rates (0 and 180 kg N ha−1), and incubation lengths (7, 14, and 28 d) with and without initial soil NH4–N included with PMNan. Soil was sampled (0–30 cm depth) before planting and N application and at V5 where 0 or 180 kg N ha−1 were applied at planting. Evaluating across all soils, PMNan was a weak predictor of EONR (R2 ≤ 0.08; RMSE, ≥67 kg N ha−1), but the predictability improved (15%) when soils were grouped by texture. Using PMNan and initial soil NH4–N as separate explanatory variables improved EONR predictability (11–20%) in fine-textured soils only. Delaying PMNan sampling from pre-plant to V5 regardless of N fertilization improved EONR predictability by 25% in only coarse-textured soils. Increasing PMNan incubations beyond 7 d modestly improved EONR predictability (R2 increased ≤0.18, and RMSE was reduced ≤7 kg N ha−1). Alone, PMNan predicts EONR poorly, and the improvements from partitioning soils by texture and including initial soil NH4–N were relatively low (R2 ≤ 0.33; RMSE ≥ 68 kg N ha−1) compared with other tools for N fertilizer recommendations

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied

A Public–Industry Partnership for Enhancing Corn Nitrogen Research and Datasets: Project Description, Methodology, and Outcomes

Due to economic and environmental consequences of N lost from fertilizer applications in corn (Zea mays L.), considerable public and industry attention has been devoted to the development of N decision tools. Needed are research and databases and associated metadata, at numerous locations and years to represent a wide geographic range of soil and weather scenarios, for evaluating tool performance. The goals of this research were to conduct standardized corn N rate response field studies to evaluate the performance of multiple public-domain N decision tools across diverse soils and environmental conditions, develop and publish new agronomic science for improved crop N management, and train new scientists. The geographic scope, scale, and unique collaborative arrangement warrant documenting details of this research. The objectives of this paper are to describe how the research was undertaken, reasons for the methods, and the project’s anticipated value. The project was initiated in a partnership between eight U.S. Midwest land-grant universities, USDA-ARS, and DuPont Pioneer. Research using a standardized protocol was conducted over the 2014 through 2016 growing seasons, yielding a total of 49 sites. Preliminary observations of soil and crop variables measured from each site revealed a magnitude of differences in soil properties (e.g., texture and organic matter) as well as differences in agronomic and economic responses to applied N. The project has generated a valuable dataset across a wide array of weather and soils that allows investigators to perform robust evaluation of N use in corn and N decision tools

Relating four‐day soil respiration to corn nitrogen fertilizer needs across 49 U.S. Midwest fields

Soil microbes drive biological functions that mediate chemical and physical processes necessary for plants to sustain growth. Laboratory soil respiration has been proposed as one universal soil health indicator representing these functions, potentially informing crop and soil management decisions. Research is needed to test the premise that soil respiration is helpful for profitable in‐season nitrogen (N) rate management decisions in corn (Zea mays L.). The objective of this research was two‐fold: (i) determine if the amount of N applied at the time of planting effected soil respiration, and (ii) evaluate the relationship of soil respiration to corn yield response to fertilizer N application. A total of 49 N response trials were conducted across eight states over three growing seasons (2014–2016). The 4‐day Comprehensive Assessment of Soil Health (CASH) soil respiration method was used to quantify soil respiration. Averaged over all sites, N fertilization did not impact soil respiration, but at four sites soil respiration decreased as N fertilizer rate applied at‐planting increased. Across all site‐years, soil respiration was moderately related to the economical optimum N rate (EONR) (r2 = 0.21). However, when analyzed by year, soil respiration was more strongly related to EONR in 2016 (r2 = 0.50) and poorly related for the first two years (r2 \u3c 0.20). These results illustrate the factors influencing the ability of laboratory soil respiration to estimate corn N response, including growing‐season weather, and the potential of fusing soil respiration with other soil and weather measurements for improved N fertilizer recommendations

Volunteering under population uncertainty

There is ample evidence that the number of players can have an important impact on the cooperation and coordination behavior of people facing social dilemmas. With extremely few exceptions, the literature on cooperation assumes common knowledge about who is a player and how many players are involved in a certain situation. In this paper, we argue that this assumption is overly restrictive, and not even very common in real-world cooperation problems. We show theoretically and experimentally that uncertainty about the number of players in a Volunteer's Dilemma increases cooperation compared to a situation with a certain number of players. We identify additional behavioral mechanisms amplifying and impairing the effect