Predicting corn tiller development in restrictive environments can be achieved to enhance defensive management decision tools for producers

IntroductionWhile globally appreciated for reliable, intensification-friendly phenotypes, modern corn (Zea mays L.) genotypes retain crop plasticity potential. For example, weather and heterogeneous field conditions can overcome phenotype uniformity and facilitate tiller expression. Such plasticity may be of interest in restrictive or otherwise variable environments around the world, where corn production is steadily expanding. No substantial effort has been made in available literature to predict tiller development in field scenarios, which could provide insight on corn plasticity capabilities and drivers. Therefore, the objectives of this investigation are as follows: 1) identify environment, management, or combinations of these factors key to accurately predict tiller density dynamics in corn; and 2) test outof-season prediction accuracy for identified factors.MethodsReplicated field trials were conducted in 17 diverse site-years in Kansas (United States) during the 2019, 2020, and 2021 seasons. Two modern corn genotypes were evaluated with target plant densities of 25000, 42000, and 60000 plants ha -1. Environmental, phenological, and morphological data were recorded and evaluated with generalized additive models.ResultsPlant density interactions with cumulative growing degree days, photothermal quotient, mean minimum and maximum daily temperatures, cumulative vapor pressure deficit, soil nitrate, and soil phosphorus were identified as important predictive factors of tiller density. Many of these factors had stark non-limiting thresholds. Factors impacting growth rates and photosynthesis (specifically vapor pressure deficit and maximum temperatures) were most sensitive to changes in plant density. Out-of-season prediction errors were seasonally variable, highlighting model limitations due to training datasets.DiscussionThis study demonstrates that tillering is a predictable plasticity mechanism in corn, and therefore could be incorporated into decision tools for restrictive growing regions. While useful for diagnostics, these models are limited in forecast utility and should be coupled with appropriate decision theory and risk assessments for producers in climatically and socioeconomically vulnerable environments

Corn yield components can be stabilized via tillering in sub-optimal plant densities

IntroductionCrop plasticity is fundamental to sustainability discussions in production agriculture. Modern corn (Zea mays L.) genetics can compensate yield determinants to a small degree, but plasticity mechanisms have been masked by breeder selection and plant density management preferences. While tillers are a well-known source of plasticity in cereal crops, the functional trade-offs of tiller expression to the hierarchical yield formation process in corn are unknown. This investigation aimed to further dissect the consequences of tiller expression on corn yield component determination and plasticity in a range of environments from two plant fraction perspectives – i) main stalks only, considering potential functional trade-offs due to tiller expression; and ii) comprehensive (main stalk plus tillers). MethodsThis multi-seasonal study considered a dataset of 17 site-years across Kansas, United States. Replicated field trials evaluated tiller presence (removed or intact) in two hybrids (P0657AM and P0805AM) at three target plant densities (25000, 42000, and 60000 plants ha-1). Record of ears and kernels per unit area and kernel weight were collected separately for both main stalks and tillers in each plot. ResultsIndicated tiller contributions impacted the plasticity of yield components in evaluated genotypes. Ear number and kernel number per area were less dependent on plant density, but kernel number remained key to yield stability. Although ear number was less related to yield stability, ear source and type were significant yield predictors, with tiller axillary ears as stronger contributors than main stalk secondary ears in high-yielding environments. DiscussionsCertainly, managing for the most main stalk primary ears possible – that is, optimizing the plant density (which consequently reduces tiller expression), is desirable to maximize yields. However, the demonstrated escape from the deterministic hierarchy of corn yield formation may offer avenues to reduce corn management dependence on a seasonally variable optimum plant density, which cannot be remediated mid-season

Favorable Team Scores Under the Team-Based Learning Paradigm: A Statistical Artifact?

Team-based learning (TBL) is a highly structured method of active small group learning and consists of identical tests taken individually and in groups. We reviewed literature to determine the scoring methods used for individual and group testing. Twenty-eight percent of the studies used multiple choice formats for the individual test and formats that allow for immediate feedback and corrective scoring for the group test. We calculated the expected value of both individual and group tests and found that by default groups will score higher than individuals, even if there is no actual difference in performance. We urge TBL practitioners to use statistically equivalent scoring for individual and group testing

Statistical indicators and state–space population models predict extinction in a population of bobwhite quail

Early warning systems of extinction thresholds have been developed for and tested in microcosm experiments, but have not been applied to populations of wild animals. We used state–space population models and a statistical indicator to detect a transcritical bifurcation extinction threshold in a population of bobwhite quail (Colinus virginianus) located in an agricultural region experiencing habitat deterioration and loss. The extinction threshold was detectible using two independent data sets. We compared predictions from state–space population models to predictions from a statistical indicator and found that predictions were corroborated. Using state–space population models, we estimated that our study population crossed the extinction threshold in 2010 (2002–2036; 95 % confidence intervals [CI]) using the whistle count (WC) data set and in 2008 (1999–2064; 95 % CI) using the Breeding Bird Survey (BBS) data. With the statistical indicator, we estimated that the extinction threshold will be crossed in 2018 (2004–2031; 95 % CI) using the WC data and will be crossed in 2012 (2006–2018; 95%CI) using the BBS data. We expect extinction in our study population soon after crossing the extinction threshold, but the time to extinction and potential reversibility of the threshold are unknown. Our results suggest that neither small nor decreasing population size will warn of the transcritical bifurcation extinction threshold. We suggest that managers of wildlife populations in regions experiencing land use change should try to predict extinction thresholds and make management decisions to ensure the persistence of the species