The role of variation in genetic susceptibility to soybean rust on the photosynthetic competence of infected leaves.

Three soybean rust infection types have been reported: 1) tan lesions indicate a compatible and susceptible reaction, 2) red- brown (RB) lesion type has been associated with few non-sporulating uredinia representing a resistant reaction, and 3) immune reaction with no visible evidence of infection. Differences among cultivars would imply genotypic differences in tolerance, which can be define as the ability of the host to endure the presence of the pathogen with reduced disease symptoms and/or damages. Soybean rust-induced yield loss has been associated with reduction in light interception due to premature leaf loss and the lesions on the remaining green leaves. Incorporation of the effect of the pathogen on photosynthetic efficiency of disease leaves from different soybean cultivars in model to predict production might increase the accuracy and precision of the estimates supply by this model. Bastiaans (1991) proposed the concept of a "virtual lesion" and developed a simple model Y= (1-x)? to fit empirical data and describe the relationship between the reduction in relative photosynthesis in a diseased leaf (Y), and the disease severity (x). The value of â indicates whether the effect of disease on photosynthesis is higher (? > 1), lower (? < l) or equal (? = 1) to that accounted for by the observed diseased area. Reliable estimate of disease effects on growth and yield depends on the ability to accurate quantify the parameter ?. The objective of the current study was to determine the role of host-plant variation in disease susceptibility on soybean leaf gas exchange in field and control environment studies. The specific objectives are to quantify the ? value for SBR-infected leaves as influenced by plant growing conditions and genetic variation in disease susceptibility.Edição do Proceedings of the National Soybean Rust Symposium, New Orleans, 2009

Testing of crop Models for Accurate Predictions of Evapotranspiration and crop Water Use

All crop models, whether site-specific or global-gridded and regardless of crop, simulate daily crop transpiration and soil evaporation during the crop life cycle, resulting in seasonal crop water use. Modelers use several methods for predicting daily potential evapotranspiration (ET), including FAO-56, Penman-Monteith, Priestley-Taylor, Hargreaves, full energy balance, and transpiration water efficiency. They use extinction equations to partition energy to soil evaporation or transpiration, depending on leaf area index. Most models simulate soil water balance and soil-root water supply for transpiration, and limit transpiration if water uptake is insufficient, and thereafter reduce dry matter production. Comparisons among multiple crop and global gridded models in the Agricultural Model Intercomparison and Improvement Project (AgMIP) show surprisingly large differences in simulated ET and crop water use for the same climatic conditions. Model intercomparisons alone are not enough to know which approaches are correct. There is an urgent need to test these models against field-observed data on ET and crop water use. It is important to test various ET modules/equations in a model platform where other aspects such as soil water balance and rooting are held constant, to avoid compensation caused by other parts of models. The CSM-CROPGRO model in DSSAT already has ET equations for Priestley-Taylor, Penman-FAO-24, Penman-Monteith-FAO-56, and an hourly energy balance approach. In this work, we added transpiration-efficiency modules to DSSAT and AgMaize models and tested the various ET equations against available data on ET, soil water balance, and season-long crop water use of soybean, fababean, maize, and other crops where runoff and deep percolation were known or zero. The different ET modules created considerable differences in predicted ET, growth, and yield

Rehabilitation of orphaned Asian elephant (Elephas maximus maximus) calves in Sri Lanka

Approximately 6,000 (13%) of the global Asian elephants live in Sri Lanka and human elephant conflict (HEC) is intense. Due to HEC, around 150 elephants die and 14 elephants are orphaned per year. The Elephant Transit Home (ETH) in Sri Lanka was established in 1995 to rehabilitate orphaned elephants with the aim to release them back to the wild. The ETH management ensures minimum human contact and that calves are free to roam in a diverse habitat composed of water reservoirs, forests, and grasslands. During the last 22 years, the ETH has received 308 orphaned calves, and 178 (58%) of them were less than six months old. There were 130 (42%) and seven (4%) mortalities before and during rehabilitation, respectively. The ETH has released 103 elephant calves back to the wild and they are closely monitored using VHF and GPS collars. So far, eight deaths of released elephants and 16 births from released females have been recorded. Surviving and breeding in the wild and integrating with wild elephants are the major indicators of success of this rehabilitation program

Direct human influence on atmospheric CO2 seasonality from increased cropland productivity

Ground- and aircraft-based measurements show that the seasonal amplitude of Northern Hemisphere atmospheric carbon dioxide (CO2) concentrations has increased by as much as 50 per cent over the past 50 years1, 2, 3. This increase has been linked to changes in temperate, boreal and arctic ecosystem properties and processes such as enhanced photosynthesis, increased heterotrophic respiration, and expansion of woody vegetation4,5, 6. However, the precise causal mechanisms behind the observed changes in atmospheric CO2 seasonality remain unclear2, 3, 4. Here we use production statistics and a carbon accounting model to show that increases in agricultural productivity, which have been largely overlooked in previous investigations, explain as much as a quarter of the observed changes in atmospheric CO2 seasonality. Specifically, Northern Hemisphere extratropical maize, wheat, rice, and soybean production grew by 240 per cent between 1961 and 2008, thereby increasing the amount of net carbon uptake by croplands during the Northern Hemisphere growing season by 0.33 petagrams. Maize alone accounts for two-thirds of this change, owing mostly to agricultural intensification within concentrated production zones in the midwestern United States and northern China. Maize, wheat, rice, and soybeans account for about 68 per cent of extratropical dry biomass production, so it is likely that the total impact of increased agricultural production exceeds the amount quantified here