Utilizing commercial soil sensing technology for agronomic decisions

Planters with mounted proximal soil sensing systems can densely quantify seed zone soil variability. Technology now allows for real-time sensor information to control multiple row-unit functions on-the-go (e.g., planting depth). These and other developing sensor-based control systems have the potential to greatly improve correctness when planting, and therefore row-crop performance. For sensor-based control to be widely adopted, practitioners must understand the precision and utility of the systems. Therefore, research was conducted to: (i) determine how well commercially available sensors can estimate soil organic matter (OM) and whether sensor output was repeatable among sensing dates; (ii) evaluate OM prediction accuracy across selected soils and soil volumetric water contents with both a commercially-available, planter-mounted sensor, and machine learning techniques applied to multiple combinations of soil reflectance bands within the visible and near infrared spectrum; and (iii) investigate if planter and other proximal soil sensor data, in combination with topographic features, could predict field-scale corn emergence rate at varying planting depths. Results found that commercial sensors could estimate general trends in spatial variability of OM, but that some inconsistencies were associated with a "global" calibration that appeared susceptible to temporal variations in soil water content. In the controlled environment, results for sensor estimation of OM were similar to the field study. Further, results showed that spectral information within the entire range used by the commercial systems evaluated was required to consistently predict OM at varying volumetric water contents. Lastly, the field-scale agronomic analysis found that inherent soil and landscape variability drove the emergence rate response at the site. However, planter metrics were still usefulIncludes bibliographical references

Early corn stand count of different cropping systems using UAV-imagery and deep learning

Optimum plant stand density and uniformity is vital in order to maximize corn (Zea mays L.) yield potential. Assessment of stand density can occur shortly after seedlings begin to emerge, allowing for timely replant decisions. The conventional methods for evaluating an early plant stand rely on manual measurement and visual observation, which are time consuming, subjective because of the small sampling areas used, and unable to capture field-scale spatial variability. This study aimed to evaluate the feasibility of an unmanned aerial vehicle (UAV)-based imaging system for estimating early corn stand count in three cropping systems (CS) with different tillage and crop rotation practices. A UAV equipped with an on-board RGB camera was used to collect imagery of corn seedlings (~14 days after planting) of CS, i.e., minimum-till corn-soybean rotation (MTCS), no-till corn-soybean rotation (NTCS), and no-till corn-corn rotation with cover crop implementation (NTCC). An image processing workflow based on a deep learning (DL) model, U-Net, was developed for plant segmentation and stand count estimation. Results showed that the DL model performed best in segmenting seedlings in MTCS, followed by NTCS and NTCC. Similarly, accuracy for stand count estimation was highest in MTCS (R2 = 0.95), followed by NTCS (0.94) and NTCC (0.92). Differences by CS were related to amount and distribution of soil surface residue cover, with increasing residue generally reducing the performance of the proposed method in stand count estimation. Thus, the feasibility of using UAV imagery and DL modeling for estimating early corn stand count is qualified influenced by soil and crop management practices

Risky business: factor analysis of survey data – assessing the probability of incorrect dimensionalisation

This paper undertakes a systematic assessment of the extent to which factor analysis the correct number of latent dimensions (factors) when applied to ordered categorical survey items (so-called Likert items). We simulate 2400 data sets of uni-dimensional Likert items that vary systematically over a range of conditions such as the underlying population distribution, the number of items, the level of random error, and characteristics of items and item-sets. Each of these datasets is factor analysed in a variety of ways that are frequently used in the extant literature, or that are recommended in current methodological texts. These include exploratory factor retention heuristics such as Kaiser’s criterion, Parallel Analysis and a non-graphical scree test, and (for exploratory and confirmatory analyses) evaluations of model fit. These analyses are conducted on the basis of Pearson and polychoric correlations.We find that, irrespective of the particular mode of analysis, factor analysis applied to ordered-categorical survey data very often leads to over-dimensionalisation. The magnitude of this risk depends on the specific way in which factor analysis is conducted, the number of items, the properties of the set of items, and the underlying population distribution. The paper concludes with a discussion of the consequences of overdimensionalisation, and a brief mention of alternative modes of analysis that are much less prone to such problems

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

How and When Retailers’ Sustainability Efforts Translate into Positive Consumer Responses: The Interplay Between Personal and Social Factors

Contains fulltext : 179202pub.pdf (publisher's version ) (Open Access)27 juni 201