Sudarshan Dutta

ABSTRACT Water quality surveys of the U.S. have confirmed the presence of hormones and antibiotics in some surface waters. Although the reported concentrations of these substances are extremely low, there is substantial concern about their effect on aquatic species. For example, chronic exposure to estradiol (E2β) concentrations as low as 40 ng/L have been shown to cause endocrine disruption in fish. Furthermore, there is potential for contaminants to enter our drinking supply. Significant sources of hormones and antibiotics include discharge from concentrated animal feeding operations (CAFOs) and wastewater treatment plants as well as runoff from agricultural land receiving application of animal manure. Since Sussex County, Delaware is one of the leading poultry producing counties in the nation, and many farmers in the state use poultry litter as fertilizer for their crops, it is critical to study the concentrations of contaminants in surface waters. Fifty surface water (streams, lakes, and ponds) sampling locations throughout the state of Delaware were chosen based on DNREC (Delaware Department of Natural Resources and Environmental Control) data. Locations with the highest nitrogen and phosphorus levels were assumed to be associated with agriculture and wastewater sources and therefore were likely to be contaminated with hormones and antibiotics. The first set of sampling occurred in April representing high-flow conditions, and the second set will occur in September representing low-flow conditions. Water samples will be screened through the cost-effective enzyme-linked immunosorbent assay (ELISA) method followed by more rigorous analyses of selected samples using liquid chromatography-mass spectrometry (LC/MS/MS). ELISA screening includes estradiol (E2β), sulfamethazine and triclosan, while LC/MS/MS will quantify both free and conjugated forms of estrone (E1), estradiol (E2β), estriol (E3), as well as selected sulfa and tetracycline antibiotics. Initial ELISA results indicate a range of 0-40 ng/L for E2β, 0-18 μg/L for sulfamethazine, and 0-3 μg/L for triclosan; however, previous studies have shown that the ELISA method tends to overestimate due to cross-reactivity and matrix effects, and therefore we expect lower concentrations from LC/MS/MS. All LC/MS/MS results are expected by December 2011. Results will be compared to the predicted no-effect concentrations to determine whether these contaminants pose a threat to aquatic species and wildlife. ArcGIS was used to plot the measured concentrations on the Delaware state map to provide a spatial representation of the potential contamination. Watersheds were delineated for each of the sampling locations, and land use, physical characteristics of the land, and potential sources of contaminants within the watersheds have been analyzed to explore any relationships with measured contaminant concentrations.

The aim of this paper was to investigate the key factors limiting maize (Zea mays L.) productivity in eastern India to develop effective crop and nutrient management strategies to reduce yield gap. A series of farm surveys was conducted in two distinct agro-ecological zones of eastern India to evaluate the importance of crop management and structural constraints for maize productivity in a range of socio-economic settings prevalent in smallholder farms. Surveys revealed yield gap and yield variations among farms across growing seasons. Lower yields of farmers were mainly associated with farmer’s ethnic origin, availability of family labor, land ownership, legumes in cropping sequence, irrigation constraints, seed type, optimal plant population, labor and capital investment, and use of organic manure. These constraints varied strongly between sites as well as growing seasons. Stochastic Frontier Analysis suggested intensification of farm input use and removal of socio-economic and structural constraints for increasing efficiency in maize production. The use of multivariate classification and regression tree analysis revealed that maize yield was affected by multiple and interacting production constraints, differentiating the surveyed farms in six distinct resource groups. These farm types lend scope for introducing typology-specific crop management practices through appropriate participatory on-farm evaluation/trials. Summarily, this research indicated that interacting production constraints should be addressed simultaneously, considering the need of different farm types, if significant productivity improvements are to be achieved. This will be,however, more challenging for less endowed farms due to lack of social and financial capital to improve management intensity. A typology-specific farm support strategy may be formulated to offset this lack of entitlement among resource-poor farmers.