Non-marketable crops are increasingly being used as a tool to promote agroecosystem services and sustainable agriculture. Nevertheless, crops vary greatly in the traits by which they capture resources and influence the local ecosystem. Here we report on the traits and associated soil microbial communities that relate to aboveground biomass production, nutrient capture, weed suppression, erosion control and building particulate organic matter of 22 different full-season cover crops. All agroecosystem services were positively correlated with maximum canopy height and leaf area. Rooting density was positively associated with indices of bacterial diversity. While some legumes produced the greatest standing N and P in aboveground biomass, they were also poor at capturing soil nitrate and promoted high levels of potential plant fungal pathogens. Conversely, Brassicaceae crops had the lowest levels of potential plant fungal pathogens, but also suppressed saprophytic fungi and rhizobia. Thus, not all crops are equal in their ability to promote all agroecosystem services, and while some crops may be ideal for promoting a specific agroecosystem service, this could result in a trade-off with another. Nonetheless, our study demonstrates that plant functional traits are informative for the selection of crops for promoting agroecosystem services.

In humid climate regions, a short period of water deficit, especially during the vegetative growth and tuberization stages, has been found to affect potato plant growth, yield and tuber quality. However, there is still a lack of information on the impact of the water deficit duration. In this study, we examined potato plant growth, yield and tuber quality parameters with plants under 0 to 25 days of water deficit initiated at the beginnings of the vegetative growth stage and the tuberization stage, respectively. We found that for both the vegetative growth and tuberization stages, a longer water deficit duration resulted in no significant change in final plant height but significantly delayed flowering and reduced total biomass, yield, tuber dry matter content and share of large tubers. We estimate that per day of prolonged water deficit, there will be a yield loss of 3.1% and 3.4% for the vegetative growth and tuberization stages, respectively. Similarly, for per liter of irrigation water, there will be a yield increase of 16.3 g and 19.1 g for the vegetative growth and tuberization stages, respectively. Further studies are suggested to examine how supplemental irrigation can be used most effectively to mitigate the impact of water deficit on potato production in humid climate regions.

Decision making on water resources management at ungauged, especially large-scale watersheds relies on hydrological modeling. Physically based distributed hydrological models require complicated setup, calibration, and validation processes, which may delay their acceptance among decision makers. This study presents an approach to develop a simple decision support tool (DST) for decision makers and economists to evaluate multiyear impacts of land use change and best management practices (BMPs) on water quantity and quality for ungauged watersheds. The example DST developed in the present study was based on statistical equations derived from Soil and Water Assessment Tool (SWAT) simulations and applied to a small experimental watershed in northwest New Brunswick. The DST was subsequently tested against field measurements and SWAT simulations for a larger watershed. Results from DST could reproduce both field data and model simulations of annual stream discharge and sediment and nutrient loadings. The relative error of mean annual discharge and sediment, nitrate–nitrogen, and soluble-phosphorus loadings were −6, −52, 27, and −16 %, respectively, for long-term simulation. Compared with SWAT, DST has fewer input requirements and can be applied to multiple watersheds without additional calibration. Also, scenario analyses with DST can be directly conducted for different combinations of land use and BMPs without complex model setup procedures. The approach in developing DST can be applied to other regions of the world because of its flexible structure.

In agriculture-dominant watersheds, riparian ecosystems provide a wide array of benefits such as reducing soil erosion, filtering chemical compounds, and retaining sediments. Traditionally, the boundaries of riparian zones could be estimated from Digital Elevation Models (DEMs) or field surveys. In this study, we used an Unmanned Aerial Vehicle (UAV) and photogrammetry method to map the boundaries of riparian zones. We first obtained the 3D digital surface model with a UAV. We applied the Vertical Distance to Channel Network (VDTCN) as a classifier to delineate the boundaries of the riparian area in an agricultural watershed. The same method was also used with a low-resolution DEM obtained with traditional photogrammetry and two more LiDAR-derived DEMs, and the results of different methods were compared. Results indicated that higher resolution UAV-derived DEM achieved a high agreement with the field-measured riparian zone. The accuracy achieved (Kappa Coefficient, KC = 63%) with the UAV-derived DEM was comparable with high-resolution LiDAR-derived DEMs and significantly higher than the prediction accuracy based on traditional low-resolution DEMs obtained with high altitude aerial photos (KC = 25%). We also found that the presence of a dense herbaceous layer on the ground could cause errors in riparian zone delineation with VDTCN for both low altitude UAV and LiDAR data. Nevertheless, the study indicated that using the VDTCN as a classifier combined with a UAV-derived DEM is a suitable approach for mapping riparian zones and can be used for precision agriculture and environmental protection over agricultural landscapes.