Xinhua Jia

Bahiagrass crop coefficients from eddy correlation measurements in central Florida

Bahiagrass (Paspalum notatum) is a warm-season grass used primarily in pastures and along highways and other low maintenance public areas in Florida. It is also used in landscapes to some extent because of its drought tolerance. Bahiagrass can survive under a range of moisture conditions from no irrigation to very wet conditions. Its well-watered consumptive use has not been reported previously. In this study, bahiagrass crop coefficients (Kc) for an irrigated pasture were determined for July 2003 through December 2006 in central Florida. The eddy correlation method was used to estimate crop evapotranspiration (ETc) rates. The standardized reference evapotranspiration (ETo) equation (ASCE-EWRI standardization of reference evapotranspiration task committee report, 2005) was applied to calculate ETo values using on site weather data. Daily Kc values were estimated from the ratio of the measured ETc and the calculated ETo. The recommended Kc values for bahiagrass are 0.35 for January–February, 0.55 for March, 0.80 for April, 0.90 for May, 0.75 for June, 0.70 for July–August, 0.75 for September, 0.70 for October, 0.60 for November, and 0.45 for December in central Florida. The highest Kc value of 0.9 in May corresponded with maximum vapor pressure deficit conditions as well as cloud free conditions and the highest incoming solar radiation as compared to the rest of the year. During the summer (June to August), frequent precipitation events increased the cloud cover and reduced grass water use. The Kc annual trend was similar to estimated Kc values from another well-watered warm-season grass study in Florida.

Zone mapping application for precision-farming: a decision support tool for variable rate application

A web-based decision support tool, zone mapping application for precision farming (ZoneMAP, http://zonemap.umac.org), has been developed to automatically determine the optimal number of management zones and delineate them using satellite imagery and field data provided by users. Application rates, such as of fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming equipment. ZoneMAP is linked to Digital Northern Great Plains, a web-based application which hosts an archive of satellite imagery, as well as high resolution imagery from airborne sensors. Management zones created by ZoneMAP mapped natural variation of the soil organic matter and other nutrients relatively well and were consistent with zone maps created by traditional means. The results demonstrated that ZoneMAP can serve as an effective and easy-to-use tool for those who practice precision agriculture.

Predicting ECe of the saturated paste extract from value of EC1:5

He, Y., DeSutter, T., Hopkins, D., Jia, X. and Wysocki, D. A. 2013. Predicting ECeof the saturated paste extract from value of EC1:5. Can. J. Soil Sci. 93: 585-594. Many laboratories appraise soil salinity from measurement of electrical conductivity of 1:5 soil to water extract (EC1:5) due to its simplicity. However, the influence of salinity on plant growth is mainly based on electrical conductivity of saturated paste extract (ECe), so it is necessary to convert EC1:5 to ECe in order to assess plant response. The objectives of this research were to develop models relating EC1:5 and ECe under four different 1:5 equilibration methods: (1) shaking, (2) shaking plus centrifuging, (3) stirring, and (4) a United States Department of Agriculture-Natural Resources Conservation Service (2011) equilibration method. One hundred soil samples, which were all derived from glacial parent materials in North Dakota, USA, were selected for this study. Non-transformed, non-transformed separated, ln-transformed, and exponential models were developed between EC1:5 and ECe. Non-transformed, simple linear regression models had obvious segments for all equilibration methods and the residual distributions varied. Therefore, data were separated at EC of 4 dS m-1 and a quadratic curvilinear model was developed for relating EC1:5 and ECe (r2 values ranged from 0.87 to 0.93) when ECe values were less than 4 dS m-1. Although the linear model was significant (P<0.05), soils having ECe greater than 4 dS m-1 had r2 values less than 0.61. Across all soils, the ln-transformed model had r2 values greater than 0.85, which was greater than the non-transformed or exponential models. By comparison of r2, RMSE, and relative percentage difference, the separated curvilinear model that was established when salinity is less than 4 dS m-1, and ln-transformed models were superior at predicting ECe from EC1:5 data compared to non-transformed and exponential models. These results indicate that across all equilibration methods ECe can reliably be predicted from EC1:5 data for soils from this region.

Subsurface Drainage and Subirrigation Effects on Water Quality in Southeast North Dakota

Rising water tables, increased soil salinity, and poor trafficability have prompted rapid expansion of subsurface drainage in the Red River Valley of the North in eastern North Dakota and northwestern Minnesota. A conventional subsurface drainage (CD) and subirrigation (SI) field study was conducted in southeast North Dakota from 2008 to 2010 to investigate drainage and subirrigation effects on water quality. Water samples were collected biweekly from a sump pump structure (used as the water inlet and outlet) and 16 observation wells within the field. Water quality variables included chloride (Cl-), electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), sodium (Na+), orthophosphate (PO4-P), ammonium (NH4-N), nitrite and nitrate (NOx-N), Kjeldahl nitrogen (TKN), and total nitrogen (TN). A three-factor partially nested design and a general linear model with random effects were employed to compare the effects of water management treatment, distance to drain, and well locations (soil heterogeneity) on water quality. The most significant water quality difference was found at the outlet structure, where a significant difference (p < 0.001) between the CD and SI water was found for all ten variables. The water quality of the drainage water was better than the subirrigation water from the aquifer, except for the NOx-N, EC, and TDS concentrations. Well water Cl- concentrations inside the field were significantly greater in SI compared with CD water; EC, TDS, SAR, and Na+ were not. In contrast, EC, TDS, SAR, and Na+ were significantly higher at two well locations, indicating that soil heterogeneity affected the water quality. Due to SI practice, a significant difference for Cl-, SAR, and Na+ was found between the locations closest to and farthest from the drains during the SI practice, which implies that the SI process may cause soil properties to change in the future. Overall, well locations significantly affected PO4-P, NOx-N, and TN, indicating that the soil physical and chemical properties affected the water quality, and these effects could overcome the difference due to different water treatments.

Comparison of Reference Evapotranspiration Calculations for Southeastern North Dakota

Potential water consumption for irrigation scheduling in North Dakota was typically calculated from a reference Evapotranspiration (ETref) using the Jensen-Haise method and its associated crop coefficient (Kc) curves developed in the 1970’s and 1980’s. The ETref method proposed by the American Society of Civil Engineers, Environmental and Water Research Institute (ASCE-EWRI) reference evapotranspiration task force has shown to be more accurate and therefore more widely used than any other methods. However, to apply the ASCE-EWRI method for irrigation scheduling requires a corresponding change of the Kc curves associated with the Jensen-Haise method. In this paper, a comparison of ETref estimates for 11 methods, including the ASCE-EWRI and the Jensen-Haise methods, was conducted using 18 years of data collected in southeastern North Dakota. The results show that the annual ETref by the Jensen-Haise method was nearly the same as the ASCE-EWRI grass ETref, but with a higher Root Mean Square Deviation (RMSD), 0.903 mm d-1, and a lower coefficient of determination (R2) 0.8659. The ETref comparison for the growing season only shows an RMSD of 1.007 mm d-1, R2 of 0.7996 and 8.13% overestimation. The ETref by the Jensen-Haisemethod has a higher monthly ETref than the ASCE-EWRI in June, July, and August, and a lower monthly ETref for all other months in an 18 year period. The ETref comparisons also show that the modified Penman method used by the High Plains Regional Climate Center (HPRCC Penman) has the best accuracy and correlation with the ASCE-EWRI ETref method. Indeed, all alfalfa based ETref methods, including Kimberly Penman and HPRCC Penman, show better performance than the grass based ETref methods, including FAO24 Penman, FAO24 Radiation, FAO24 Blaney-Criddle, Priestley-Taylor, Hargreaves, and the Jensen-Haise methods.

Change of soil hardness and soil properties due to tile drainage in the Red River Valley of the North

Subsurface drainage (or tile drainage) is expected to expand significantly in the Red River Valley of the North, yet its effects on soil quality have not been studied. The objectives of this project are to evaluate the changes of soil hardness and physical properties overlying tile drained and undrained areas. In 2002, tile was installed in the south half of a 47 ha field located in southeastern North Dakota. During the fall of 2007, after soybeans were harvested, soil borings were taken to a depth of 2.1 m and soil samples collected at intervals of 15 cm. Two soil borings were in the undrained portion of the field and four soil borings were in the tile drained portion of the field. Soil properties were measured to determine if there were differences due to tile drainage. Soil hardness, an indicator of compaction, was measured at the site of each boring. The soil samples, taken from 15 cm increments of the soil borings, were analyzed to determine soil moisture content, texture, bulk density and saturated hydraulic conductivity. There was no significant difference of the soil bulk density, moisture content, and soil texture. However, as expected, there was large variability for each of these parameters. There was no significant difference in the saturated hydraulic conductivity, but there was great variability between increment samples. This may be due to the variability of the soil texture at different depths. Soil hardness was statistically significant only in the 0-15 cm interval (the tillage zone).

Development of Bahiagrass Crop Coefficient in a Humid Climate

Bahiagrass (Paspalum notatum) crop coefficients (Kc) were determined for July 2003 through December 2006 in central Florida. The eddy correlation method was used to estimate crop evapotranspiration (ETc) rates. The standardized reference evapotranspiration (ETo) equation (ASCE-EWRI, 2005) was applied to calculate the ETo values using weather data on site. Daily Kc values were estimated from the ratio of the measured ETc and the calculated ETo. The Kc values determined by the eddy correlation method were similar to those determined in the same region by eddy correlation method and soil water balance method. Using the measured Kc values, the recommended monthly bahiagrass Kc values were as follows: 0.35 (Jan. – Feb.), 0.55 (Mar.), 0.80 (Apr.), 0.90 (May), 0.75 (Jun.), 0.70 (Jul. – Aug.), 0.75 (Sep.), 0.65 (Oct.), 0.60 (Nov.), and 0.45 (Dec.) in central Florida.

Remote Sensing of Drivers of Spring Snowmelt Flooding in the North Central U.S.

Spring snowmelt poses an annual flood risk in nonmountainous regions, such as the northern Great Plains of North America. However, ground observations are often not sufficient to characterize the spatiotemporal variation of drivers of snowmelt floods for operational flood forecasting purposes. Remote sensing platforms are well suited to nonmountainous, low vegetation areas, and can add value by providing estimates of hydrological states important for flood prediction. In this chapter, we review the use of remote sensing observations, primarily from passive microwave instruments, to constrain drivers of spring snowmelt floods, with a special focus on the Red River of the North basin in the north central United States. While many factors affect snowmelt flooding, snow water equivalent (SWE) and fall soil moisture play a significant role in determining flood severity in the region. Methods to estimate SWE and soil moisture are summarized, and past remote sensing research conducted in the region is reviewed. Considerations for incorporation of remote sensing estimates into the operational flood forecasting workflow and models are also discussed, using the NOAA National Weather Service (NWS) North Central River Forecast Center (NCRFC) as an example.

Effects of Calcium-Based Surface Amendments on the Penetration Resistance of Subsurface Drained Sodic Soils

In the saline/sodic and sodic soils of the northern Great Plains, subsurface drainage can inadvertently result in clay particle dispersion if the surface soils are leached with rainwater. Under these conditions, penetration resistance (PR) in wet soil can be used to examine the effectiveness of free drainage (FD) vs. no drainage (ND) treatments and surface amendments consisting of a high rate of gypsum (GH), a low rate of gypsum (GL), spent lime (SL, a byproduct from the processing of sugarbeets), and no amendments [or check plots (CK)] on improving soil trafficability. The PR and soil moisture contents were determined from 0 to 45 cm depth for sodic soil plots near Wyndmere, North Dakota, during June 2015. The effects of drainage and surface amendments on PR were evaluated using analysis of variance, with gravimetric moisture content incorporated as a covariate. Significant differences were considered at p < 0.05. The mean PR values of ND (450 and 936 kPa) and FD (428 and 917 kPa) for the 0 to 15 cm and 15 to 30 cm layers, respectively, were not significantly different. The PR value for the surface 15 cm was higher for GH (485 kPa) than for the other surface amendments. In the 15 to 30 cm layer, the PR for GH (1050 kPa) was significantly higher than for GL (954 kPa), which was in turn higher than for SL (866 kPa) and CK (839 kPa). Benefits from the combined effects of drainage and surface amendments were more evident in the 15 to 30 cm layer than in the 0 to 15 cm layer. In the 0 to 15 cm layer, NDGH had PR means (498 kPa) that were similar to all other treatments but higher than for FDSL (384 kPa). In the 15 to 30 cm layer, FDGL had PR means (1007 kPa) similar to FDGH (1074 kPa) and NDGH (1027 kPa), which showed that drainage coupled with a lower gypsum rate achieved similar results as the higher rate of gypsum application.

Measurement and simulation of infiltration rates into undrained and subsurface drained soils

Abstract. Infiltration is an important process in the hydrological cycle and the main source of water for crop production. Though infiltration occurs at the soil surface, it is affected by the soil properties below the ground surface, such as soil water content and hydraulic conductivities. Accurate measurement of infiltration rate is possible when accurate soil properties within the infiltration zone (up to 30 cm below soil surface) are determined. In this study, infiltration rates were measured using a Cornel Sprinkler Infiltrometer during spring, summer and fall of 2014 in undrained and subsurface drained (i.e. tile drained) fields in Clay County, Minnesota. Because of the soil moisture difference between the undrained and tile drained fields, the measured infiltration rates were also different. Soil moisture contents were measured at 5, 15 and 30 cm depths below the soil surface in both fields using soil moisture sensors. Soil water retention curves (SWRC) were developed from soil core samples collected in both fields using Hyprop and WP4 Dewpoint Potentiometer methods. Important parameters used in infiltration modeling, including saturated water content, residual water content and van Genuchten curve fitting parameters, were also determined from the SWRC. Two models, HYDRUS 1D and HYDROL-INF, were used to simulate infiltration rates using the measured soil properties. The measured and simulated infiltration rates were compared and analyzed in order to estimate the infiltration difference between undrained and tile drained soils. Average infiltration rate was greater in tile drained soil (0.0041 cm/min) compare to undrained soil (0.0023 cm/min) from HYDROL-INF model simulations. HYDRUS 1D model simulation indicated that tile drained soil had four times greater hydraulic conductivity (0.015 cm/min) and three times greater cumulative infiltrating water amount (2.04 cm) than those of undrained soil (0.0034 cm/min and 0.62 cm, respectively). The comparison between the undrained and tile drained soils suggested that due to better soil physical conditions, subsurface drained soil had higher hydraulic conductivity as well as higher infiltrating water amount and infiltration rate than those of undrained soil.