Research Compendium: A New Standard Area Diagram Set for Assessment of Severity of Soybean Rust Improves Accuracy of Estimates and Optimizes Resource Use

Repository of for data, codes and preprint of the stud

Research Compendium: Modeling the effect of El Niño Southern Oscillation on the onset of soybean rust in Southern Brazil

Codes website: https://alvesks.github.io/paper-SBR_El_Nino

A New Standard Area Diagram Set for Assessment of Severity of Soybean Rust Improves Accuracy of Estimates and Optimizes Resource Use

Soybean rust (SBR), caused by Phakopsora pachyrhizi, is the most important yield- damaging fungal disease of soybean due to severe reduction in healthy leaf area and acceleration of leaf fall. In experimental research, SBR severity is estimated visually aided/trained by a standard area diagram (SAD) developed and validated during the mid- 2000s (Old SAD). In this study, we propose a new SAD set for SBR with six true-colour diagrams following linear increments (c.15% increments) amended with four additional diagrams at low (<10%) severities, totaling 10 diagrams (0.2%, 1%, 3%, 5%, 10%, 25%, 40%, 55%, 70%, and 84%). For evaluation, 37 raters were split into two groups. Each assessed severity in a 50-image sample (0.25% to 84%), first unaided and then using either the Old SAD or the New SAD. Accuracy, precision, and reliability of estimates improved significantly relative to unaided estimates only when aided by the New SAD (accuracy >0.95). Low precision (<0.78) and a trend of underestimation with an increase in severity were the main issues with the Old SAD, which did not differ from unaided estimates. Simulation to evaluate the impact of the errors by different methods on hypothesis tests, showed that the new SAD was more powerful for detecting the smallest difference in mean control (e.g., 70% vs. 65% disease reduction) than the Old SAD; the latter required a 2-fold increase in sample size to achieve the same power. There is a need to improve some SADs, taking advantage of new knowledge and technology to increase accuracy of the estimates, and to optimize both resource use efficiency and management decisions

Research Compendium: Effect of climate on the spatial distribution of citrus huanglongbing in Minas Gerais, Brazil

Research Compendium for the paper: "Linking climate variables to large-scale spatial pattern and risk of citrus Huanglongbing: a hierarchical Bayesian modeling approach

Linking climate variables to large-scale spatial pattern and risk of citrus Huanglongbing: a hierarchical Bayesian modeling approach

Huanglongbing (HLB) is one of the most important diseases for the citriculture in the world. Knowledge of climatic factors linked to HLB risk at the large spatial scale is limited. We gathered HLB presence/absence data from official surveys conducted in the state of Minas Gerais, Brazil, for 13 years. The total count of orange and mandarin orchards, and mean orchard area, normalized to a spatial grid of 60 cells (55 x 55 km), were derived from the same database. The monthly climate normal (1984 to 2013) on rainfall, mean temperature, and wind speed were split into rainy (September to April) and dry (May to August) seasons (annual summary was retained) were also obtained for each grid cell. Two hierarchical Bayesian modeling approaches were evaluated both based on the integrated nested Laplace approximation methodology. The first, the climate covariates model (CC model), used orchard, climate, and the spatial effect as covariates. The second, principal components (PC model), used the first three components from a PCA of all variables and the spatial effect as covariates. Both models showed an inverse relationship between posterior prevalence and mean temperature during dry season across the grid cells. Annual wind speed, as well as annual and rainy season rainfall, contributed significantly towards HLB risk, in the CC and PC models, respectively. A partial influence of neighboring regions on HLB risk was observed. These results should assist policymakers in defining regions at HLB risk and monitoring strategies to avoid further spread in the target region

Spatiotemporal spread of huanglongbing in commercial citrus orchards of Minas Gerais, Brazil

The spatiotemporal spread of citrus huanglongbing (HLB, ex greening) in Minas Gerais (MG) state, Brazil, was analyzed since its first detection in 2005. Data on the amount of eradicated plants per orchard were gathered from the georeferenced database of the state’s official HLB monitoring program. In total, 1,487 orchards (118 municipalities) have been inspected yearly up to 2018. Overall, 57.2% (64.4% of the municipalities) of the orchards were affected by HLB and a total of 459,254 plants, mainly mandarins (62.7%) and sweet oranges (35.6%), and very few lemon and acid lime (1.7%), have been eradicated. The percent of HLB-affected orchards was variable in three scenarios of citrus varieties and citrus-growing regions: 20% of sweet orange orchards in the Triângulo Mineiro (TM) region and 64.8% in the South of Minas (SM) region; and 80% of mandarins produced in the SM and 40.4% in the Central regions. The numbers of eradications were generally higher in mandarins in the SM than in sweet oranges in the TM because of the large orchard size, better management, and more recent introduction of the disease. HLB spread faster among orchards in the SM and Central regions due to a lack of HLB-oriented management, small size and proximity between each other. The disease has spread 45.9 km/year and 25.7 km/year on average in the TM and Central/SM, respectively. The HLB-affected orchards were spatially aggregated in mandarin orchards in the SM, which was not evident in sweet oranges growing the TM and SM. Research and extension resources shall be mobilized to help citrus farmers of the SM and Central regions to extend as much as possible the feasibility of the production and prevent further spread among and within mandarin orchards in Central MG. These results can be of value to further improve risk assessment of HLB spread in other regions that share similarities with those in MG state

Research Compendium: Spatiotemporal spread of Huanglongbing of citrus in Minas Gerais

Repository of the data and R scripts to conduct the analysis and produce the figures

Research Compendium: Sequential post-heading applications for controlling wheat blast: a quantitative summary of fungicide performance

Wheat blast, caused by Pyricularia oryzae Triticum (PoT) lineage, is a major constraint to wheat production, mainly in the tropics of Brazil where severe epidemics are more frequent. We analyzed disease and wheat yield data from 42 uniform field trials conducted during nine years (2012 to 2020) in order to assess whether the percent control and yield response were influenced by fungicide type, region (tropical or subtropical), and year. Six treatments were selected, all evaluated in at least 19 trials. Two fungicides were applied as solo active ingredients: MANCozeb, and TEBUconazole, and four were premixes: AZOXistrobin + TEBU, TriFLoXistrobin + PROThioconazole, TFLX + TEBU, and PYRAclostrobin + EPOXiconazole