Effect of Alternaria and Septoria spot on sunflower yield.

Leaf diseases affect crop yields. In sunflower crops, leaf spot severity can reach 100%, but the magnitude of the yield loss caused by the disease is not known. This study aimed to evaluate the effect of Alternaria and Septoria leaf spot severity on sunflower yield across different years in a humid subtropical climate. We conducted 37 experiments in Santa Maria, RS, Brazil, over 7 years. The hybrids Embrapa 122, Helio 358, Aguará 03, and Altis 99 were sowed and managed according to national crop recommendations. Severity assessments for Alternaria and Septoria spots were performed at 2- to 7-day intervals using a diagrammatic scale. We evaluated the effects of Alternaria and Septoria leaf spot severity on crop yield using upper limit graphs. The 37 experiments comprised 13 normal season crops (August to October) and 24 late season crops (November to February). The results were also classified according to the contemporaneous phases of the ENSO (El Niño Southern Oscillation): El Niño, La Niña, and Neutral. In normal season crops, severities of up to 24% do not result in yield decrease. After this, each 1% increment in disease severity produces a decrease of 66 kg ha-1 on sunflower yield. In late season crops, the reduction in productivity occurs at severities greater than 34%, with a decrease of 50 kg ha-1 for each 1% increase in combined disease severity. The highest severity values and lowest yields, both in the normal and late season crops, occurred in El Niño years.

Weather-based coffee leaf rust apparent infection rate modeling.

Brazil is the major coffee producer in the world, with 2 million hectares cropped, with 75% of this area with Coffea arabica and 25% with Coffea canephora. Coffee leaf rust (CLR) is one of the main diseases that cause yield losses by reducing healthy leaf area. As CLR is highly influenced by weather conditions, this study aimed to determine the best linearization model to estimate the CLR apparent infection rate, to correlate CLR infection rates with weather variables, and to develop and assess the performance of weather-based infection rate models to be used as a disease warning system. The CLR epidemic was analyzed for 88 site-seasons, while progress curves were assessed by linear, monomolecular, logistic, Gompertz, and exponential linearization models for apparent infection rate determination. Correlations between CLR infection rates and weather variables were conducted at different periods. From these correlations, multiple linear regressions were developed to estimate CLR infection rates, using the most weather-correlated variables. The Gompertz growth model had the best fit with CLR progress curves. Minimum temperature and relative humidity were the weather variables most correlated to infection rate and, therefore, chosen to compose a CLR forecast system. Among the models developed, the one for the condition of high coffee yield at a narrow row spacing was the best, with only 9.4% of false negative occurrences for all the months assessed.