Management of grape powdery mildew with an intelligent sprayer and sulfur

Wine grapes are an important agricultural commodity in the Pacific Northwest where grape powdery mildew (GPM) is one of the main disease problems. The efficacy of different sulfur concentrations and different output volumes from an air blast sprayer retrofitted with the Intelligent Spray System (ISS) were evaluated for the management of GPM. The ISS consists of a LiDAR sensor, Doppler speed sensor, embedded computer, flow controller, and individual pulse-width-modulation solenoid valves at each nozzle. GPM cluster severity ranged from 55% to 75% across all trials in the study when using the ISS at its default spray rate of 62.5 ml m-3 and micronized sulfur at 6 g L-1, which was significantly higher than all other fungicide treatments, but lower than non-treated controls. Similarly, leaf incidence values were highest on non-treated vines, followed by micronized sulfur at 6 g L-1 applied at 62.5 ml m-3 , with all other fungicide treatments being significantly lower in all trials. Using the ISS at the 62.5 ml m-3 rate and a rotation of locally systemic fungicides resulted in the lowest observed GPM leaf incidence, and average cluster severity of 11% in both 2019 and 2020, the lowest cluster severity of all fungicide treatments tested. GPM control using the ISS and micronized sulfur was equivalent to a constant-rate air blast treatment at 6 g L-1 when the spray rate of the ISS was increased to 125ml m-3, or if the concentration of sulfur was increased to 24 g L-1. In those cases, the amount of sulfur applied to vines was at or above the minimum label rate from bloom until the end of the season, or the entire season, respectively. This study has shown that sufficient disease control cannot always be expected when mixing pesticides at the same rate as would be used for a constant-rate sprayer in a variable rate sprayer, especially when using contact fungicides like sulfur . With appropriate adjustments, the variable-rate ISS can be a useful tool to reduce pesticide quantities, water required for mixing, and as a result labor, as fewer trips to refill for a given spray event are required.

Clustering and Smoothing Pipeline for Management Zone Delineation Using Proximal and Remote Sensing

In precision agriculture (PA) practices, the accurate delineation of management zones (MZs), with each zone having similar characteristics, is essential for map-based variable rate application of farming inputs. However, there is no consensus on an optimal clustering algorithm and the input data format. In this paper, we evaluated the performances of five clustering algorithms including k-means, fuzzy C-means (FCM), hierarchical, mean shift, and density-based spatial clustering of applications with noise (DBSCAN) in different scenarios and assessed the impacts of input data format and feature selection on MZ delineation quality. We used key soil fertility attributes (moisture content (MC), organic carbon (OC), calcium (Ca), cation exchange capacity (CEC), exchangeable potassium (K), magnesium (Mg), sodium (Na), exchangeable phosphorous (P), and pH) collected with an online visible and near-infrared (vis-NIR) spectrometer along with Sentinel2 and yield data of five commercial fields in Belgium. We demonstrated that k-means is the optimal clustering method for MZ delineation, and the input data should be normalized (range normalization). Feature selection was also shown to be positively effective. Furthermore, we proposed an algorithm based on DBSCAN for smoothing the MZs maps to allow smooth actuating during variable rate application by agricultural machinery. Finally, the whole process of MZ delineation was integrated in a clustering and smoothing pipeline (CaSP), which automatically performs the following steps sequentially: (1) range normalization, (2) feature selection based on cross-correlation analysis, (3) k-means clustering, and (4) smoothing. It is recommended to adopt the developed platform for automatic MZ delineation for variable rate applications of farming inputs.

An Optimization-Based Method for the Explicit Production Data Analysis of Gas Wells

This work aims at the exploration of production data analysis (PDA) methods without iterations. It can overcome limitations of the advanced type curve analysis relying on the iterative calculation of material-balance pseudotime and current explicit methods reckoning on specific production schedule assumptions. The dynamic material balance equation (DMBE) is strictly proved by the integral variable substitution based on the gas flow equation under the boundary dominated flow (BDF) condition and the static material balance equation (SMBE) of a gas reservoir. We introduce the pseudopressure level function γ(p) and the recovery factor function R(p) to rewrite the DMBE in terms of observed variable Y and estimated variable Ye; then the PDA can be transformed into an optimization problem of minimizing the error between Y and Ye. An optimization-based method for the explicit production data analysis of gas wells (OBM-EPDA), therefore, is developed in the paper, capable of determining the BDF constant and gas reserves explicitly and accurately for variable rate and/or variable flowing pressure systems. Three stimulated cases demonstrate the applicability and validity of OBM-EPDA with small errors less than 1% for estimated values of both reserves and Y. Not second to previous studies, the field case analysis further proves its practicability. It is shown that the nonlinear relation of γ to R can be represented by a polynomial function merely dependent on the inherent properties of the gas production system even before sorting out the production data. The errors of observed variable Y provided by OBM-EPDA facilitate the data quality control, and the elimination of outliers not subject to the BDF condition improves the reliability of the analysis. For various gas systems producing whether at a constant rate, a constant bottomhole pressure (BHP), or under variable rate and variable BHP conditions, the proposed method gives insights into the well-controlled volume and production capacity of the gas well whether in a low-pressure or high-pressure gas reservoir, where the compressibilities of rock and bound water are considered.

A profile of nontraditional agricultural real estate lender activity in the secondary market

PurposeThis paper uses novel data from a secondary market to assess how loans from nontraditional agricultural real estate lenders (NARELs) differ from traditional sources. Over $2 billion in loans from these entities were purchased by the secondary market between 2011 and 2020, but a lack of data has prevented a robust understanding of how these institutions operate.Design/methodology/approachThe authors review loans from nontraditional lenders through their lifecycle in the secondary market from application to purchase and performance.FindingsThis paper finds no observable differences between nontraditional and traditional volumes with regards to borrower credit characteristics, loan approval rates, interest margins and loan performance. It finds significant differences between loan volumes and variable rate product use.Originality/valueThis is the first paper to use internal lender data to review nontraditional agricultural real estate loans and is the first analysis of nontraditional agricultural volumes in the secondary market.

COVID-19 clusters in schools: frequency, size, and transmission rates from crowdsourced exposure reports

The role of schools in the spread of the COVID-19 pandemic is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports for several jurisdictions in the US and Canada, providing estimates of mean and dispersion for cluster size, whilst factoring in imperfect ascertainment. Our parameter estimates are robust to variations in ascertainment fraction. We use these estimates in three ways: i) to explore how uneven the distribution of cases is among different clusters in different jurisdictions (that is, what fraction of cases are in the 20% largest clusters), ii) to estimate how long it will be until we see a cluster a given size in jurisdiction, and iii) to determine the distribution of instantaneous transmission rate β among different index case. We show how these latter distribution can be used in simulations of school transmission where we explore the effect of different interventions, in the context of highly variable transmission rates.