Evaluation of seedling age and nutrient sources on phenology, yield and agrometeorological indices for sweet corn (Zea mays saccharata L.)

The field experiment was conducted during Kharif season of 2020 at Agronomy farm of Faculty of Agriculture, Wadura, SKUAST-K to study the influence of age of seedling and sources of nutrients on phenology, yield and agrometeorological indices for sweet corn. The experiment included two factors viz. age of seedlings (12, 22 and 32 days old seedling) and sources of nutrients (control, RDF, 50 percent RDF + FYM @ 12 t ha⁻¹, 50 percent RDF + vermi-compost @ 4 t ha⁻¹ and 50 percent RDF + poultry manure @ 2 t ha⁻¹) tested in RCBD with three replications. Transplanting 12 days old seedlings required maximum number of days to attain different phenological stages, thereby accumulated maximum heat units followed by 22 days old seedlings. While as transplanting 22 days old seedling recorded significantly highest HUE, HTUE, PTUE and HyTUE and consequently resulted in the highest green cob and biological yield compared to other ages of seedlings. Among various sources of nutrients, application of 50 per cent RDF + poultry manure @ 2 t ha⁻¹ took maximum number of days to attain various phenophases thereby accumulated maximum heat units and registered highest HUE, HTUE, PTUE and HyTUE followed by application of 100 per cent RDF.

Towards understanding temporal and spatial dynamics of Bactrocera oleae (Rossi) infestations using decade-long agrometeorological time series

Insect dynamics depend on temperature patterns, and therefore, global warming may lead to increasing frequencies and intensities of insect outbreaks. The aim of this work was to analyze the dynamics of the olive fruit fly, Bactrocera oleae (Rossi), in Tuscany (Italy). We profited from long-term records of insect infestation and weather data available from the regional database and agrometeorological network. We tested whether the analysis of 13 years of monitoring campaigns can be used as basis for prediction models of B. oleae infestation. We related the percentage of infestation observed in the first part of the host-pest interaction and throughout the whole year to agrometeorological indices formulated for different time periods. A two-step approach was adopted to inspect the effect of weather on infestation: generalized linear model with a binomial error distribution and principal component regression to reduce the number of the agrometeorological factors and remove their collinearity. We found a consistent relationship between the degree of infestation and the temperature-based indices calculated for the previous period. The relationship was stronger with the minimum temperature of winter season. Higher infestation was observed in years following warmer winters. The temperature of the previous winter and spring explained 66 % of variance of early-season infestation. The temperature of previous winter and spring, and current summer, explained 72 % of variance of total annual infestation. These results highlight the importance of multiannual monitoring activity to fully understand the dynamics of B. oleae populations at a regional scale.