Zisheng Xing

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes.

Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain high-resolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had signifi...

Influences of Sampling Methodologies on Pesticide-Residue Detection in Stream Water

Traditional grab sampling (GS) used widely in the study of water quality has been found lacking in spatial and temporal resolution for pesticide residue monitoring in stream water. The objectives of this article are to present a hydrograph-based sampling approach and compare it with traditional GS according to sensitivity at temporal and spatial scales and maximum concentrations of pesticide residues detected in-stream. Data collected from streams receiving water from three nested watersheds located in northwestern New Brunswick, Canada, were used in this study. The results showed that the hydrograph-based sampling method detected 20 to 30 % more pesticide cases than GS for rainfall events causing runoff. Grab sampling significantly underestimated average concentrations of pesticide residues by 50 % and maximum concentrations by 1 to 3 orders of magnitude. Using a modified sampler design, the spatial and temporal variability of pesticide residues was more accurately captured by hydrograph-based sampling, and therefore its use in monitoring programs is recommended.

Model prediction of soil drainage classes based on digital elevation model parameters and soil attributes from coarse resolution soil maps

High-resolution soil drainage maps are important for crop production planning, forest management, and environmental assessment. Existing soil classification maps tend to only have information about the dominant soil drainage conditions and they are inadequate for precision forestry and agriculture planning purposes. The objective of this research was to develop an artificial neural network (ANN) model for producing soil drainage classification maps at high resolution. Soil profile data extracted from coarse resolution soil maps (1:1 000 000 scale) and topographic and hydrological variables derived from digital elevation model (DEM) data (1:35 000 scale) were considered as candidates for inputs. A high-resolution soil drainage map (1:10 000) of the Black Brook Watershed (BBW) in northwestern New Brunswick (NB), Canada, was used to train and validate the ANN model. Results indicated that the best ANN model included average soil drainage classes, average soil sand content, vertical slope position (VSP), sedi...

Field Performance of Nine Soil Water Content Sensors on a Sandy Loam Soil in New Brunswick, Maritime Region, Canada

An in situ field test on nine commonly-used soil water sensors was carried out in a sandy loam soil located in the Potato Research Center, Fredericton, NB (Canada) using the gravimetric method as a reference. The results showed that among the tested sensors, regardless of installation depths and soil water regimes, CS615, Trase, and Troxler performed the best with the factory calibrations, with a relative root mean square error (RRMSE) of 15.78, 16.93, and 17.65%, and a r2 of 0.75, 0.77, and 0.65, respectively. TRIME, Moisture Point (MP917), and Gopher performed slightly worse with the factory calibrations, with a RRMSE of 45.76, 26.57, and 20.41%, and a r2 of 0.65, 0.72, and 0.78, respectively, while the Gypsum, WaterMark, and Netafim showed a frequent need for calibration in the application in this region.

SWAT Setup with Long-Term Detailed Landuse and Management Records and Modification for a Micro-Watershed Influenced by Freeze-Thaw Cycles

In the widely used soil and water assessment tool (SWAT), the standard hydrological response units (HRUs) delineation method has low spatial resolution with respect to model inputs and outputs and renders difficulties in using long-term detailed landuse and management records. In addition, the modified universal soil loss equation (MUSLE) uses a constant K-factor which cannot address seasonal variation in soil erodibility caused by freeze-thaw cycles in cold regions. The current study presents a simple method to incorporate detailed landuse and management inputs in SWAT. The method delineates HRUs based on field boundaries and associates each HRU with a particular field. As a result, long-term detailed records can be incorporated into the SWAT management files. In addition, the existing MUSLE in SWAT was modified by introducing a variable K-factor to address effects of freeze-thaw cycles on soil erosion for cold regions. This modified version of SWAT was calibrated and validated for an agricultural micro-watershed, i.e., Black Brook Watershed in New Brunswick, Canada. The results showed that, compared with the standard HRU-delineation method, field-based HRU-delineation method was able to improve landuse and management practice input accuracy for SWAT and save time and effort for long-term simulation, and provide high resolution outputs in the watershed. As a result, the field-based HRU-delineation method can facilitate decision making not only at the subbasin scale but also at the field scale. In addition, results showed that sediment loading simulation accuracy was improved with the modified-MUSLE compared with the original-MUSLE.

Effects of Hay Mulch on Soil Properties and Potato Tuber Yield under Irrigation and Nonirrigation in New Brunswick, Canada

AbstractOrganic mulching and irrigation are considered to be important soil conservation and agricultural practices. However, the effectiveness of these practices on soil properties and potato production in the relatively cool, moist maritime region of Canada has not yet been well studied. In the growing seasons of 2000–2003, a field experiment was conducted at the Potato Research Centre, Agriculture and Agri-Food Canada, Fredericton, New Brunswick, to assess the benefits of hay mulching on a loamy sand soil under potato production. Treatments consisted of four levels of hay mulching: 0, 2.25, 4.5, and 9.0  Mg ha-1 with four replicates under irrigation or nonirrigation. Results showed that hay mulching could help conserve soil moisture in nonirrigation treatments with an increase of mean soil moisture content by 5.7 to 9.5% under 2.25 to 9.0  Mg ha-1 of hay mulch relative to a control. The greatest conservation effect on soil water in our region would be achieved with a mulch rate of 5.6  Mg ha-1. Under i...