Do Cover Crop And Soil-Mediated Legacy Influence Succeeding Wheat Production?

ABSTRACT

Biotic interaction of cover crops (CCs) can have a legacy effect on succeeding crops mediated by changes in nutrient dynamics. Depending on species, CCs influence nitrogen (N) dynamics by sequestering N and subsequent N release. Interactions of three CC species, Austrian Pea (Pisum sativum L.), winter rye (Secale cereal L.), and winter camelina (Camelina sativa L.), and three different soils were studied under greenhouse conditions on wheat (Triticum aestivum L.) grain yield and soil N availability. CCs were grown for two months and then incorporated, followed by the planting of wheat. CC biomass production ranged from 0.10 to 2.05 Mg ha^?1 in this order by species: Pea> Rye> Camelina. Biomass production by soil was in the order of Casselton>Ada>Minot. Succeeding wheat grain yield and grain N uptake was highest under pea in the order of pea>camelina>control>rye. Rye reduced grain yield and N uptake. Wheat yield ranged from 2.19 to 3.24 Mg ha^?1 depending on CC species-soil interaction. The N balance showed a 3–79% higher N surplus with the CCs. The N balance ranged from 78 kg N ha^?1 for the control to 140 kg N ha^?1 for pea. N surplus was greater for a pea in all soils, indicating pea can be regarded as an effective cover that can efficiently recycle N and provide additional agronomic benefits. Greater N balance with CCs shows that CCs can increase the amount of N accounted for in the system, which can significantly affect the N dynamics throughout the growing season.     

GIS-Based Emission Inventory, Dispersion Modeling, and Assessment for Source Contributions of Particulate Matter in an Urban Environment

The Industrial Source Complex Short Term (ISCST3) model was used to discern the sources responsible for high PM₁₀ levels in Kanpur City, a typical urban area in the Ganga basin, India. A systematic geographic information system-based emission inventory was developed for PM₁₀ in each of 85 grids of 2?×?2 km. The total emission of PM₁₀ was estimated at 11 t day^?1 with an overall breakup as follows: (a) industrial point sources, 2.9 t day^?1 (26%); (b) vehicles, 2.3 t day^?1 (21%); (c) domestic fuel burning, 2.1 t day^?1 (19%); (d) paved and unpaved road dust, 1.6 t day^?1 (15%); and the rest as other sources. To validate the ISCST3 model and to assess air-quality status, sampling was done in summer and winter at seven sampling sites for over 85 days; PM₁₀ levels were very high (89?C632 ??g m^?3). The results show that the model-predicted concentrations are in good agreement with observed values, and the model performance was found satisfactory. The validated model was run for each source on each day of sampling. The overall source contribution to ambient air pollution was as follows: vehicular traffic (16%), domestic fuel uses (16%), paved and unpaved road dust (14%), and industries (7%). Interestingly, the largest point source (coal-based power plant) did not contribute significantly to ambient air pollution. The reason might be due to release of pollutant at high stack height. The ISCST3 model was shown to produce source apportionment results like receptor modeling that could generate source apportionment results at any desired time and space resolution.