Visual exploration of emotional scenes in aging during a free visualization task depending on arousal level of scenes.

International audienceResearch on emotion suggests that the attentional preference observed toward the negative stimuli in young adults tends to disappear in normal aging and, sometimes, to shifts towards a preference for positive stimuli. However, this age-related effect called the positivity effect may be modulated by several factors, such as the arousal level of stimuli. The present study investigated visual exploration of natural scenes of different emotional valence in three age groups (young, middle-aged and older adults) depending on arousal level of scenes using an eye-tracking paradigm. Participants visualized pairs of emotional scenes either in low or high arousal condition. In contrast with the literature, the preliminary results revealed a reduction in prevalence of negative stimuli relative to other ones in older adults regardless of the arousal conditions. No difference between young adults and middle aged adults was observed

Determining Economic Optimum Soil Sampling Density for Potassium Fertilizer Management in Soybean: A Case Study in the U.S. Mid-South

Determining the number of samples to collect in a field to develop soil-test K (STK) maps that are sufficiently accurate for profit-maximizing fertilizer rate prescription maps is complex. The decision also hinges on the application method—variable rate or uniform rate (VRT vs. URT). Using a 400 m2 fishnet grid on a 26.3-ha irrigated soybean field, the authors compared sampling densities ranging from 5 to 60 samples or 5.3 ha/sample to 0.40 ha/sample. Subsequently, the authors simulated yields based on STK maps generated with that range of samples taken to generate i) associated profit-maximizing fertilizer-K rates (K*) that varied by grid with VRT, or ii) a single fertilizer rate based on field-average STK with URT, to compare revenue less fertilizer cost (NR) across VRT, URT, and sampling strategy. With more information, NR increased at a diminishing rate as crop needs could be better matched to fertilizer needs with greater detail in STK maps with VRT. Also, fertilizer use with URT was higher than VRT given the field-specific distribution of STK. Regardless of the sampling strategy, NR was higher for VRT than URT, however, that benefit was smaller than the upcharges for VRT equipment. Marginal benefits from added soil sampling were smaller than their marginal cost leading to an optimal least-cost, 5-sample strategy and URT. Changing one of the 5 sampling locations, however, revealed unreliable field average STK estimates. Since soil samples inform about several macronutrients, splitting soil sampling charges across K and P profitably justified sampling near every 1.5 ha with URT

Multispectral UAS Data Accuracy for Different Radiometric Calibration Methods

Unmanned aircraft systems (UAS) allow us to collect aerial data at high spatial and temporal resolution. Raw images are taken along a predetermined flight path and processed into a single raster file covering the entire study area. Radiometric calibration using empirical or manufacturer methods is required to convert raw digital numbers into reflectance and to ensure data accuracy. The performance of five radiometric calibration methods commonly used was investigated in this study. Multispectral imagery was collected using a Parrot Sequoia camera. No method maximized data accuracy in all bands. Data accuracy was higher when the empirical calibration was applied to the processed raster rather than the raw images. Data accuracy achieved with the manufacturer-recommended method was comparable to the one achieved with the best empirical method. Radiometric error in each band varied linearly with pixel radiometric values. Smallest radiometric errors were obtained in the red-edge and near-infrared (NIR) bands. Accuracy of the composite indices was higher for the pixels representing a dense vegetative cover in comparison to a lighter cover or bare soil. Results provided a better understanding of the advantages and limitations of existing radiometric calibration methods as well as the impact of the radiometric error on data quality. The authors recommend that researchers evaluate the performance of their radiometric calibration before analyzing UAS imagery and interpreting the results

Using Apparent Electrical Conductivity to Delineate Field Variation in an Agroforestry System in the Ozark Highlands

Greater adoption and better management of spatially complex, conservation systems such as agroforestry (AF) are dependent on determining methods suitable for delineating in-field variability. However, no work has been conducted using repeated electromagnetic induction (EMI) or apparent electrical conductivity (ECa) surveys in AF systems within the Ozark Highlands of northwest Arkansas. As a result, objectives were to (i) evaluate spatiotemporal ECa variability; (ii) identify ECa-derived soil management zones (SMZs); (iii) establish correlations among ECa survey data and in situ, soil-sensor volumetric water content, sentential site soil-sample EC, and gravimetric water content and pH; and (iv) determine the optimum frequency at which ECa surveys could be conducted to capture temporal changes in field variability. Monthly ECa surveys were conducted between August 2020 and July 2021 at a 4.25 ha AF site in Fayetteville, Arkansas. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged from 1.8 to 18.0 and 3.1 to 25.8 mS m−1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. The largest measured ECa values occurred within the local drainage way or areas of potential groundwater movement, and the smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. The PRP and HCP mean ECa, standard deviation (SD), and coefficient of variation (CV) were unaffected (p > 0.05) by either the weather or growing/non-growing season. K-means clustering delineated three precision SMZs that were reflective of areas with similar ECa and ECa variability. Results from this study provided valuable information regarding the application of ECa surveys to quantify small-scale changes in soil properties and delineate SMZs in highly variable AF systems

Relationships among apparent electrical conductivity and plant and terrain data in an agroforestry system in the Ozark Highlands

Abstract Minimal research has been conducted relating apparent electrical conductivity (ECa) surveys to plant and terrain properties in agroforestry systems. Objectives were to identify: (i) ECa–forage yield, –tree growth, and –terrain attribute relationships within ECa‐derived soil management zones (SMZs) and (ii) terrain attributes that drive ECa variability within a 20‐year‐old, 4.25‐ha, agroforestry system in the Ozark Highlands of northwest Arkansas. The average of 12 monthly perpendicular (PRP) and horizontal coplanar (HCP) ECa surveys (August 2020 to July 2021) and 14 terrain attributes were obtained. Tree diameter at breast height (DBH) and height (TH) measurements were made in December 2020 and March 2021, respectively, and forage yield samples were collected during Summer 2018 and 2019. Apparent EC‐tree property relationships were generally stronger within the whole site (averaged across tree property and ECa configuration, |r| = 0.38) than within the SMZs (averaged across tree property, ECa configuration, and SMZ, |r| = 0.27). The strength of the SMZs’ terrain‐attribute‐PRP‐ECa relationships were 9% to 205% greater than that for the whole site. In whole‐site, multi‐linear regressions, slope length and steepness factor (10.5%), mid‐slope (9.4%), and valley depth (7.2%) had the greatest influence (i.e., percent of total sum of squares) on PRP ECa variability, whereas valley depth (15.3%), wetness index (11.9%), and mid‐slope (11.2%) had the greatest influence on HCP ECa variability. Results show how ECa relates to plant (i.e., DBH, TH, and forage yield) and terrain data within SMZs in agroforestry systems with varying topography and could be used to precisely manage agroforestry systems

Cultivating trust in technology-mediated sustainable agricultural research

We formed the Precision Sustainable Agriculture (PSA) team to conduct interdisciplinary research and technology development to improve adoption and practice of knowledge-intensive sustainable agricultural practices such as cover cropping. In this paper, we share our approach to cultivating trust among diverse stakeholders (researchers, farmers, extensionists, agricultural and information specialists, private and public entities) vested in agricultural data collection, management, and use. Our trust framework describes how we aim to be trusted with data (through preserving privacy and increasing stakeholder agency) and trusted in the process (through practicing transparency and accountability). It is operationalized through a series of social and technical infrastructures. Our project governance, stakeholder engagement tools and activities, and technology development methods aim to promote transparency and accountability in our process. We use a maturity model to govern data acquisition to ensure that only robust, privacy-preserving technologies are deployed on our partner farms and describe evolving mechanisms for handling data with varying sensitivity. Finally, we share preliminary work aimed at anticipating data use, and identify challenges on the horizon for cultivating trust in agricultural technologies and data-driven agriculture.This article is published as Raturi, Ankita, Jennifer J. Thompson, Victoria Ackroyd, Carlene A. Chase, Brian W. Davis, Robert Myers, Aurelie Poncet et al. "Cultivating trust in technology‐mediated sustainable agricultural research." Agronomy Journal (2021). doi:10.1002/agj2.20974. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted