Cover crop performance under a changing climate in continuous corn system over Nebraska.

Fall-planted cover crop (CC) within a continuous corn (Zea mays L.) system offers potential agroecosystem benefits, including mitigating the impacts of increased temperature and variability in precipitation patterns. A long-term simulation using the Decision Support System for Agrotechnology Transfer model was made to assess the effects of cereal rye (Secale cereale L.) on no-till continuous corn yield and soil properties under historical (1991-2020) and projected climate (2041-2070) in eastern Nebraska. Local weather data during the historical period were used, while climate change projections were based on the Canadian Earth System Model 2 dynamically downscaled using the Canadian Centre for Climate Modelling and Analysis Regional Climate Model 4 under two representative concentration pathways (RCP), namely, RCP4.5 and RCP8.5. Simulations results indicated that CC impacts on corn yield were nonsignificant under historical and climate change conditions. Climate change created favorable conditions for CC growth, resulting in an increase in biomass. CC reduced N leaching under climate change scenarios compared to an average reduction of 60% (7 kg ha- 1 ) during the historical period. CC resulted in a 6% (27 mm) reduction in total water in soil profile (140 cm) and 22% (27 mm) reduction in plant available water compared to no cover crop during historical period. CC reduced cumulative seasonal surface runoff/soil evaporation and increased the rate of soil organic carbon buildup. This research provides valuable information on how changes in climate can impact the performance of cereal rye CC in continuous corn production and should be scaled to wider locations and CC species.

Light and Electron Microscopy Studies Elucidating Mechanisms of Tomato Leaf Infection by Pseudocercospora fuligena.

The fungal pathogen Pseudocercospora fuligena, known to affect tomatoes in the tropics and subtropics, has been reported from temperate climates including the United States and Turkey in recent years. In this study, an isolate from fresh tomatoes and the disease it causes were characterized and infection mechanisms investigated. Macroscopically, both sides of tomato leaves show indistinct effuse patches but prolific production of fuliginous lesions is conspicuous on the abaxial side first but also on the adaxial side later on as infection progressed. Microscopically, fascicles of conidiophores (11-128 µm × 3.5-9 µm) arising from stromata and conidia with up to 12 septations were observed. Molecular characterization of the isolate revealed high homology (99.8%) to other P. fuligena isolated from tomatoes in Turkey. Out of the 10 media tested, P. fuligena grew significantly well and sporulated better on unsealed tomato oatmeal agar and carrot leaf decoction agar, both supplemented with CaCO3. Direct transfer of conidia from profusely sporulating lesions was the easiest and quickest method of isolation for in-vitro studies. Light and scanning electron microscopy on cleared and intact tomato leaves further confirmed stomatal penetration and egress as well as prevalence of primary and secondary infection hyphae. In situ, blocked stomatal aperture areas of 154, 401, and 2,043 µm2 were recorded at 7, 12, and 17 days after inoculation, respectively. With the recent expanded horizon of the pathosystem and its consequential impact, such studies will be useful for a proper diagnosis, identification and management of the disease on tomato worldwide.

Simulating agroecosystem soil inorganic nitrogen dynamics under long-term management with an improved SWAT-C model.

Despite the extensive application of the Soil and Water Assessment Tool (SWAT) for water quality modeling, its ability to simulate soil inorganic nitrogen (SIN) dynamics in agricultural landscapes has not been directly verified. Here, we improved and evaluated the SWAT-Carbon (SWAT-C) model for simulating long-term (1984-2020) dynamics of SIN for 40 cropping system treatments in the U.S. Midwest. We added one new nitrification and two new denitrification algorithms to the default SWAT version, resulting in six combinations of nitrification and denitrification options with varying performance in simulating SIN. The combination of the existing nitrification method in SWAT and the second newly added denitrification method performed the best, achieving R, NSE, PBIAS, and RMSE of 0.63, 0.29, -4.7 %, and 16.0 kg N ha-1, respectively. This represents a significant improvement compared to the existing methods. In general, the revised SWAT-C model's performance was comparable to or better than other agroecosystem models tested in previous studies for assessing the availability of SIN for plant growth in different cropping systems. Sensitivity analysis showed that parameters controlling soil organic matter decomposition, nitrification, and denitrification were most sensitive for SIN simulation. Using SWAT-C for improved prediction of plant-available SIN is expected to better inform agroecosystem management decisions to ensure crop productivity while minimizing the negative environmental impacts caused by fertilizer application.