Irrigation Scheduling Based on Wireless Sensors Output and Soil-Water Characteristic Curve in Two Soils.

Data-driven irrigation planning can optimize crop yield and reduce adverse impacts on surface and ground water quality. We evaluated an irrigation scheduling strategy based on soil matric potentials recorded by wireless Watermark (WM) sensors installed in sandy loam and clay loam soils and soil-water characteristic curve data. Five wireless WM nodes (IRROmesh) were installed at each location, where each node consisted of three WM sensors that were installed at 15, 30, and 60 cm depths in the crop rows. Soil moisture contents, at field capacity and permanent wilting points, were determined from soil-water characteristic curves and were approximately 23% and 11% for a sandy loam, and 35% and 17% for a clay loam, respectively. The field capacity level which occurs shortly after an irrigation event was considered the upper point of soil moisture content, and the lower point was the maximum soil water depletion level at 50% of plant available water capacity in the root zone, depending on crop type, root depth, growth stage and soil type. The lower thresholds of soil moisture content to trigger an irrigation event were 17% and 26% in the sandy loam and clay loam soils, respectively. The corresponding soil water potential readings from the WM sensors to initiate irrigation events were approximately 60 kPa and 105 kPa for sandy loam, and clay loam soils, respectively. Watermark sensors can be successfully used for irrigation scheduling by simply setting two levels of moisture content using soil-water characteristic curve data. Further, the wireless system can help farmers and irrigators monitor real-time moisture content in the soil root zone of their crops and determine irrigation scheduling remotely without time consuming, manual data logging and frequent visits to the field.

Carbon dioxide flux as affected by tillage and irrigation in soil converted from perennial forages to annual crops.

Among greenhouse gases, carbon dioxide (CO(2)) is one of the most significant contributors to regional and global warming as well as climatic change. A field study was conducted to (i) determine the effect of soil characteristics resulting from changes in soil management practices on CO(2) flux from the soil surface to the atmosphere in transitional land from perennial forages to annual crops, and (ii) develop empirical relationships that predict CO(2) flux from soil temperature and soil water content. The CO(2) flux, soil temperature (T(s)), volumetric soil water content (theta(v)) were measured every 1-2 weeks in no-till (NT) and conventional till (CT) malt barley and undisturbed soil grass-alfalfa (UGA) systems in a Lihen sandy loam soil (sandy, mixed, frigid Entic Haplustoll) under irrigated and non-irrigated conditions in western North Dakota. Soil air-filled porosity (epsilon) was calculated from total soil porosity and theta(v) measurements. Significant differences in CO(2) fluxes between land management practices (irrigation and tillage) were observed on some measurement dates. Higher CO(2) fluxes were detected in CT plots than in NT and UGA treatments immediately after rainfall or irrigation. Soil CO(2) fluxes increased with increasing soil moisture (R(2)=0.15, P<0.01) while an exponential relationship was found between CO(2) emission and T(s) (R(2)=0.59). Using a stepwise regression analysis procedure, a significant multiple regression equation was developed between CO(2) flux and theta(v), T(s) (CO(2) flux = e(-3.477+0.123T(s)+6.381theta)(v); R(2)=0.68, P

Accuracy and performance of three water quality models for simulating nitrate nitrogen losses under corn.

Simulation models can be used to predict N dynamics in a soil-water-plant system. The simulation accuracy and performance of three models: LEACHM (Leaching Estimation And CHemistry Model), NCSWAP (Nitrogen and Carbon cycling in Soil, Water And Plant), and SOILN to predict NO3-N leaching were evaluated and compared to field data from a 5-yr experiment conducted on a Hagerstown silt loam (fine, mixed, mesic Typic Hapludalf). Nitrate N losses past 1.2 m from N-fertilized and manured corn (Zea mays L.) were measured with zero-tension pan lysimeters for 5 yr. The models were calibrated using 1989-1990 data and validated using 1988-1989, 1990-1991, 1991-1992, and 1992-1993 NO3-N leaching data. Statistical analyses indicated that LEACHM, NCSWAP, and SOILN models were able to provide accurate simulations of annual NO3-N leaching losses below the 1.2-m depth for 8, 9, and 7 of 10 cases, respectively, in the validation years. The inaccuracy in the models' annual simulations for the control and manure treatments seems to be related to inadequate description of processes of N and C transformations in the models' code. The overall performance and accuracy of the SOILN model were worse than those of LEACHM and NCSWAP. The root mean square error (RMSE) and modeling efficiency (ME) were 10.7 and 0.9, 9.5 and 0.93, and 20.7 and 0.63 for LEACHM, NCSWAP, and SOILN, respectively. Overall, the three models have the potential to predict NO3-N losses below 1.2-m depth from fertilizer and manure nitrogen applied to corn without recalibration of models from year to year.

SOIL-SOILN simulations of water drainage and nitrate nitrogen transport from soil core lysimeters.

Water resources protection from nitrate nitrogen (NO3-N) contamination is an important public concern and a major national environmental issue. The abilities of the SOIL-SOILN model to simulate water drainage and nitrate N fluxes from orchardgrass (Dactylis glomerata L.) were evaluated using data from a 3-yr field experiment. The soil is classified as a Hagerstown silt loam soil (fine, mixed, semiactive, mesic Typic Hapludalf). Nitrate losses below the 1-m depth from N-fertilized grazed orchardgrass were measured with intact soil core lysimeters. Five N-fertilizer treatments consisted of a control, urine application in the spring, urine application in the summer, urine application in the fall, and feces application in the summer. The SOIL-SOILN models were evaluated using water drainage and nitrate flux data for 1993-1994, 1994-1995, and 1995-1996. The N rate constants from a similar experiment with inorganic fertilizer and manure treatments under corn (Zea mays L.) were used to evaluate the SOILN model under orchardgrass sod. Results indicated that the SOIL model accurately simulated water drainage for all three years. The SOILN model adequately predicted nitrate losses for three urine treatments in each year and a control treatment in 1994-1995. However, it failed to produce accurate simulations for two control treatments in 1993-1994 and 1995-1996, and feces treatments in all three years. The inaccuracy in the simulation results for the control and feces treatments seems to be related to an inadequate modeling of N transformation processes. In general, the results demonstrate the potential of the SOILN model to predict NO3-N fluxes under pasture conditions using N transformation rate constants determined through the calibration process from corn fields on similar soils.

Nitrogen fertilizer rate and crop management effects on nitrate leaching from an agricultural field in central Pennsylvania.

Eighteen pan lysimeters were installed at a depth of 1.2 m in a Hagerstown silt loam soil in a corn field in central Pennsylvania in 1988. In 1995, wick lysimeters were also installed at 1.2 m depth in the same access pits. Treatments have included N fertilizer rates, use of manure, crop rotation (continuous corn, corn-soybean, alfalfa-corn), and tillage (chisel plow-disk, no-till). The leachate data were used to evaluate a number of nitrate leaching models. Some of the highlights of the 11 years of results include the following: 1) growing corn without organic N inputs at the economic optimum N rate (EON) resulted in NO3--N concentrations of 15 to 20 mg l(-1) in leachate; 2) use of manure or previous alfalfa crop as partial source of N also resulted in 15 to 20 mg l(-1) of NO3--N in leachate below corn at EON; 3) NO3--N concentration in leachate below alfalfa was approximately 4 mg l(-1); 4) NO3--N concentration in leachate below soybeans following corn was influenced by fertilizer N rate applied to corn; 5) the mass of NO3--N leached below corn at the EON rate averaged 90 kg N ha(-1) (approx. 40% of fertilizer N applied at EON); 6) wick lysimeters collected approximately 100% of leachate vs. 40-50% collected by pan lysimeters. Coefficients of variation of the collected leachate volumes for both lysimeter types were similar; 7) tillage did not markedly affect nitrate leaching losses; 8) tested leaching models could accurately predict leachate volumes and could be calibrated to match nitrate leaching losses in calibration years, but only one model (SOILN) accurately predicted nitrate leaching losses in the majority of validation treatment years. Apparent problems with tested models: there was difficulty estimating sizes of organic N pools and their transformation rates, and the models either did not include a macropore flow component or did not handle macropore flow well.