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Seed is a critically important basic input of agriculture, because sowing healthy seeds is essential to food production. Using high quality seed enables less use of synthetic pesticides in the field. Seedborne pathogens can reduce yield quantity and quality of the crops produced. Seed treatments protect plant seedlings from pathogen attacks at emergence and at the early growth stages, contributing to healthy crop plants and good yield. However, there is increased concern about the application of synthetic pesticides to seeds, while alternatives are becoming increasingly addressed in seedborne pathogen research. A series of strategies based on synthetic fungicides, natural compounds, biocontrol agents (BCAs), and physical means has been developed to reduce seed contamination by pathogens. The volume of research on seed treatment has increased considerably in the past decade, along with the search for green technologies to control seedborne diseases. This review focuses on recent research results dealing with protocols that are effective in the management of seedborne pathogens. Moreover, the review illustrated an innovative system for routine seed health testing and need-based cereal seed treatment implemented in Norway. [ABSTRACT FROM AUTHOR]

Coffee leaf rust (CLR) is the most important disease in coffee and is caused by Hemileia vastatrix. The use of a diagrammatic scale helps people more effectively evaluate rust severity and improves disease measurement by evaluators. Our goal was to develop a new scale with colored pictures and seven disease levels (0; 0.1–1.0; 1.1–2.0; 2.1–5.0; 5.1–10.0; 10.1–25.0; >25.0%) and then compare the severity results of CLR on arabica coffee leaves with those based on three other scales. Three evaluations were performed by ten different evaluators. The first assessment was performed without a scale. On the same day, raters performed four assessments with each of the four diagrammatic scales. The third evaluation was performed at seven-day intervals. We analyzed the statistics with linear regression and Lin's concordance correlation. The evaluators using the proposed scale improved the precision, accuracy and reproducibility of the evaluations and reduced residual distribution when compared to the evaluators who did not use the proposed diagrammatic scale or who used the other scales. Overall, the proposed diagrammatic scale is a tool that can assist users in producing a disease estimate close to the real value of CLR on arabica coffee leaves. [ABSTRACT FROM AUTHOR]