Assessing the influence of abiotic factors and leaf-level properties on the stability of growing-season canopy greenness in a deciduous forest

Maps depicting spatial pattern in the stability of summer greenness could advance understanding of how forest ecosystems will respond to global changes such as a longer growing season. Declining summer greenness, or “greendown”, is spectrally related to declining near-infrared reflectance and is observed in most remote sensing time series to begin shortly after peak greenness at the end of spring and extend until the beginning of leaf coloration in autumn,. Understanding spatial patterns in the strength of greendown has recently become possible with the advancement of Landsat phenology products, which show that greendown patterns vary at scales appropriate for linking these patterns to proposed environmental forcing factors. This study tested two non-mutually exclusive hypotheses for how leaf measurements and environmental factors correlate with greendown and decreasing NIR reflectance across sites. At the landscape scale, we used linear regression to test the effects of maximum greenness, elevation, slope, aspect, solar irradiance and canopy rugosity on greendown. Secondly, we used leaf chemical traits and reflectance observations to test the effect of nitrogen availability and intrinsic water use efficiency on leaf-level greendown, and landscape-level greendown measured from Landsat. The study was conducted using Quercus alba canopies across 21 sites of an eastern deciduous forest in North America between June and August 2014. Our linear model explained greendown variance with an R2=0.47 with maximum greenness as the greatest model effect. Subsequent models excluding one model effect revealed elevation and aspect were the two topographic factors that explained the greatest amount of greendown variance. Regression results also demonstrated important interactions between all three variables, with the greatest interaction showing that aspect had greater influence on greendown at sites with steeper slopes. Leaf-level reflectance was correlated with foliar δ13C (proxy for intrinsic water use efficiency), but foliar δ13C did not translate into correlations with landscape-level variation in greendown from Landsat. Therefore, we conclude that Landsat greendown is primarily indicative of landscape position, with a small effect of canopy structure, and no measureable effect of leaf reflectance. With this understanding of Landsat greendown we can better explain the effects of landscape factors on vegetation reflectance and perhaps on phenology, which would be very useful for studying phenology in the context of global climate change

Bacterial communities of the specialty crop phyllosphere: response to biological soil amendment use, rainfall, and insect visitation

Microorganisms in the plant rhizosphere, the zone under the influence of roots, and phyllosphere, the aboveground plant habitat, exert a strong influence on plant growth, health, and protection. Tomatoes and cucumbers are important players in produce safety, and the microbial life on their surfaces may contribute to their fitness as hosts for foodborne pathogens such as Salmonella enterica and Listeria monocytogenes. External factors such as agricultural inputs and environmental conditions likely also play a major role. However, the relative contributions of the various factors at play concerning the plant surface microbiome remain obscure, although this knowledge could be applied to crop protection from plant and human pathogens. Recent advances in genomic technology have made investigations into the diversity and structure of microbial communities possible in many systems and at multiple scales. Using Illumina sequencing to profile particular regions of the 16S rRNA gene, this study investigates the influences of climate and crop management practices on the field-grown tomato and cucumber microbiome. The first research chapter (Chapter 3) involved application of 4 different soil amendments to a tomato field and profiling of harvest-time phyllosphere and rhizosphere microbial communities. Factors such as water activity, soil texture, and field location influenced microbial community structure more than soil amendment use, indicating that field conditions may exert more influence on the tomato microbiome than certain agricultural inputs. In Chapter 4, the impact of rain on tomato and cucumber-associated microbial community structures was evaluated. Shifts in bacterial community composition and structure were recorded immediately following rain events, an effect which was partially reversed after 4 days and was strongest on cucumber fruit surfaces. Chapter 5 focused on the contribution of insect visitors to the tomato microbiota, finding that insects introduced diverse bacterial taxa to the blossom and green tomato fruit microbiome. This study advances our understanding of the factors that influence the microbiomes of tomato and cucumber. Farms are complex environments, and untangling the interactions between farming practices, the environment, and microbial diversity will help us develop a comprehensive understanding of how microbial life, including foodborne pathogens, may be influenced by agricultural conditions.

Agricultural Water Law in Maryland: The Water Appropriation Application Process and Use in a Time of Drought

To conserve and protect the State's water resources the State of Maryland controls the appropriation or use of its surface waters and groundwater. State law requires all agricultural operations to comply with the water appropriation permitting process, including traditional forms of agriculture, livestock and poultry operations, nursery operations and aquaculture.

Agriculture, Environmental Incentive Payments, and Water Quality in the Chesapeake Bay

Nonpoint sources (NPS) pollution from agriculture is the leading source of water quality impairment in U.S. rivers and streams, and a major contributor to lakes, wetlands, estuaries and coastal waters (U.S. EPA 2016). Using data from a survey of farmers in Maryland, this dissertation examines the effects of a cost sharing policy designed to encourage adoption of conservation practices that reduce NPS pollution in the Chesapeake Bay watershed. This watershed is the site of the largest Total Maximum Daily Load (TMDL) implemented to date, making it an important setting in the U.S. for water quality policy. I study two main questions related to the reduction of NPS pollution from agriculture. First, I examine the issue of additionality of cost sharing payments by estimating the direct effect of cover crop cost sharing on the acres of cover crops, and the indirect effect of cover crop cost sharing on the acres of two other practices: conservation tillage and contour/strip cropping. A two-stage simultaneous equation approach is used to correct for voluntary self-selection into cost sharing programs and account for substitution effects among conservation practices. Quasi-random Halton sequences are employed to solve the system of equations for conservation practice acreage and to minimize the computational burden involved. By considering patterns of agronomic complementarity or substitution among conservation practices (Blum et al., 1997; USDA SARE, 2012), this analysis estimates water quality impacts of the crowding-in or crowding-out of private investment in conservation due to public incentive payments. Second, I connect the econometric behavioral results with model parameters from the EPA’s Chesapeake Bay Program to conduct a policy simulation on water quality effects. I expand the econometric model to also consider the potential loss of vegetative cover due to cropland incentive payments, or slippage (Lichtenberg and Smith-Ramirez, 2011). Econometric results are linked with the Chesapeake Bay Program watershed model to estimate the change in abatement levels and costs for nitrogen, phosphorus and sediment under various behavioral scenarios. Finally, I use inverse sampling weights to derive statewide abatement quantities and costs for each of these pollutants, comparing these with TMDL targets for agriculture in Maryland.