FREE ELECTRONIC LIBRARY - Theses, dissertations, documentation

Pages:     | 1 |   ...   | 18 | 19 || 21 | 22 |

«University of California Division of Agriculture and Natural Resources Committee of Experts on Dairy Manure Management September 2003 February 2004, ...»

-- [ Page 20 ] --

–  –  –

a : empirical constant (cm-1) (set to be 0.105 for both crops), L : the z-coordinate of the soil surface (cm) We assume that both crops only take up NH4-N and NO3-N (no organic N), that uptake is passive and that it is not limited by high concentrations (The maximally allowable concentrations are arbitrarily set to be 100,000 mg N cm-3 for NH4-N and NO3-N).

H.2.4 The partition of the calculated period initial conditions Though we should consider two crops (corn and winter crop), HYDRUS-1D can deal with only one crop per simulation. Hence, we divided the total simulation period into seven

individual simulation periods that were run sequentially:

–  –  –

The initial conditions for Winter 99 to Corn 01 simulations are set to the terminal (final) conditions from the previous simulation period. The initial conditions for the first simulation (Corn 98) are the linearly interpolated values of measured data (volumetric water content, NH4-N concentration, and NO3-N concentration) on April 27th, 1998. The initial org-N concentration is set to be zero (Corn 98).

The simulated unsaturated domain includes the soil profile (root zon and the deeper vadose zone and stretches from the soil surface to the water table at 3m depth. The entire profile is assumed to be hydrologically homogeneous. The bottom boundary (3m depth) is defined as a water table boundary condition (h= 0).

H.2.5 Boundary conditions at the soil surface (1) Rainfall, Evaporation, and Transpiration We use CIMIS (California Irrigation Management Information System) data at Modesto (Station #71) for rainfall (Figure 1) and potential evapotranspiration (Figure 3). The product of potential evapotranspiration and crop coefficient (UCCE, 1987) calculates the crop evapotranspiration (Figure 3).

The crop evapotranspiration (ET) is separated into evaporation (E) and transpiration (T) by the following equation (Campbell, 1985),

–  –  –

LAI : leaf area index (set to be 2.0, E / T = 0.25) The crop coefficient is characterized by five time points (A, B, C, D, and E) that are indicated

in UCCE (1987) (Table 1):

–  –  –

(2) Irrigation and Fertilization Rates The amounts of irrigation, Org-N and NH4-N concentrations in manure, and chemical nitrogen were measured on most occasions (Figure 4). Missing data are replaced using the following procedure.

Preirrigation amounts on 1998/5/4 are assumed to be averages of preirrigation amounts for corn in 1999 and 2000 (1999/6/12 and 2000/5/6). NH4-N and Org-N amounts on 1998/5/4 are assumed to be averages of concentrations of the first applied lagoon water in summer in 1999, 2000 and 2001 (1999/7/14, 2000/6/14 and 2001/7/3). Preirrigation for winter crop in 1998 was conducted until 1998/9/10 (personal communication, M. Campbell-Mathews). Planting date of winter crop in 1998 was 1998/9/22. So, this irrigation date is assumed to be 1998/9/10.

Irrigation amounts on this date are assumed to be averages of the first irrigation for the winter crop in 1999, 2000 and 2001 (1999/11/1, 2000/10/23 and 2001/10/17). Irrigation amounts on 1998/10/22, 1999/3/1, 2000/3/1 and 2001/4/17 are assumed to be the average of measured irrigation amounts for winter crop in 1999, 2000 and 2001.

The source of applied NH4-N for corn in 1998 (1998/6/20-1998/8/6), on 1999/3/1 and 2000/3/1 was commercial fertilizer (anhydrous ammonia). The source of the nitrate was the fresh irrigation water in the canal. The fresh water source is from mountain snowmelt stored in reservoirs then supplemented by varying amounts of pumped shallow groundwater. Nitrate is mostly from groundwater, hence nitrate concentrations vary from irrigation to irrigation.

Missing values (red in Table 2) are due to missing samples of the canal water or missing measurements, or both. Missing values are assumed to be the average of measured nitrate concentrations before and after their respective date (see Figure 5).

H.3 Calibration and Results H.3.1 Calibration of Hydraulic Properties of Soil

–  –  –

Nakamura et al., Committee of Consultants Report December 2002 soil particle density is assumed to be 2.6. The rest of the parameters are determined by inverse solution (calibration). Calibration of these hydraulic parameters was done separately for three

different calibration periods:

Case 1: Corn 98 (1998/4/28 – 1998/9/14, Target: 40 soil water content data) Case 2: Corn 99 (1999/8/9 – 1999/9/12, Target: 72 groundwater level data at a well near the field) Case 3: Corn 01 (2001/8/2 – 2001/9/21, Target: 15 soil water content data including measurements before and after irrigation) The results of the inverse solution are shown in Table 3.The results of the inverse solution are

summarized as follows:

Case 1: Figure 7 indicates that the simulation of soil water content is very good for the calibration period (1998), but also for the validation years 1999 and 2000. Only in 2001, the soil water content predicted from this calibration was not adequate.

Case 2: This calibration, which did not use soil water content but groundwater level data for 1999 yielded parameters and validation results similar to Case 1 (Figure 8). However, calibration against groundwater levels was not satisfactory (Fig.10). The calibrated saturated hydraulic conductivity is 100cm d-1 which is the same as the minimum value specified for the calibration indicating convergence problems during the calibration. The laboratory measured value for the saturated hydraulic conductivity is 1358cm d-1 using the falling head method.

This calibration should be rerun in future work by combining the unsaturated model with a groundwater model that can also account for the lateral flow of water at and below the water table. Lateral groundwater flow was not considered in this calibration.

Case 3: Figure 9 shows that this calibration provides a good fit to the measured 2001 data used for the calibration, but also for the (predicted) validation periods 1998, 1999, and 2000.

The parameter set obtained from the calibration is consistent with the measured soil texture, classified as loamy sand according to grain size analysis (sand : 81%, silt : 15%, clay : 4%).

Calibrated soil water retention curves from the Case 3 calibration match best with typical retention curves for loamy sand (Figure 6). Thus, we use the calibrated parameters obtained from the Case 3 calibration for the transport calibration and predictive work in the following sections.

–  –  –

The isotherm coefficient of NH4-N is estimated by calibrating to plant N uptake data and is estimated to be 0.52 (Figure 11).

H.3.3 Sensitivity Analysis The isotherm coefficient of Org-N (kOrg-N) and the first-order reaction coefficients of mineralization (kmin), nitrification (knit), and denitrification (kden) are unknown. These parameters are determined by calibration via an extensive sensitivity analysis. For the sensitivity analysis, sequential simulations of the seven crop periods were performed for a total of 1470 parameter combinations: 7 different kOrg-N levels, 5 different kmin levels, 6 different knit levels, and 7 different kden levels. All possible combinations were simulated and root mean square erros (RMSE) mapped across the mineralization-nitrification parameter space (Figure 12a-g). A set of six maps were generated for each of the six denitrification rates.

Seven set (pages) of maps were generated, one for each of the seven sorption coefficients for organic nitrogen. Transformation rates are reported in units of [days-1].

On all maps, the least RMSE (best simulations) is observed in the upper left map, where the denitrification rate is zero (no denitrification). With non-zero denitrification rates, the predicted results deviated from measured data. Errors (RMSE) become larger with larger denitrification rates. It is therefore likely that denitrification plays a negligible role.

Within each of the maps, including the upper left maps, the least RMSE is observed around log(knit) ~ -1 and is relatively insensitive to log(kmin) values above -2. Very similar patterns are observed across all sorption rates for organic N, krgN. However, the minimum RMSE observed within individual maps for kden = 0 is lower as the sorption rates increases. Hence, the overall optimal result (best case) are obtained for large organic N sorption kOrg-N=1000, relatively fast mineralization rates kmin~1.6, rapid nitrification, knit=0.1, and no denitrification kden=0. The largest RMSE (worst parameter set) at high organic N sorption is obtained for kOrg-N=10000, very fast mineralization and nitrification rates kmin=100, knit=0.0001, and high denitrification rates kden=1.

To illustrate the sensitivity of the predictions to this wide parameter range, ammonium and nitrate profiles and their transient behavior during 1998-2001 are shown for both the best case (Figure 13-14) and worst case (Figure 15-16) scenario. Clearly, the worst case parameter set provides unacceptable results. Hence, relatively high significance can be given to the best case parameter set, at least to the order of magnitude of the individual parameters. Modeling results are more sensitive to nitrification rates (approximately 0.1 d-1), but much less sensitive to mineralization (anywhere in the range of 0.1 d-1 – 100 d-1). This would support the Nakamura et al., Committee of Consultants Report December 2002 conclusion that the vast portion of the organic nitrogen applied mineralizes very quickly – with half-lives of a few tens of days at the most, perhaps even shorter.

With the optimized parameters, we simulated the nitrogen fluxes across the water table. All of the nitrogen at the water table is in form of nitrate. The total groundwater loading over the seven cropping seasons is nearly 15 mg N cm-2 (1500 kg N / ha) (Figure 17), approximately the same as the cumulative crop nitrogen uptake (Figure 18). Nitrogen leaching decreased after 1998 due to improved nutrient management practices (Figure 18, Figure 20), while plant nitrogen uptake continued at the same rate throughout 1998-2001. Cumulative mineralization and nitrification are shown in Figure 19, which reflects the model assumption of zero transformation during the winter months. It is conceivable that allowing for some transformation during the winter months (e.g., at 25% of the summer rate) would impact the calibration results towards slower overall mineralization rates. This issue should be pursued in future research. Clearly, though, mineralization occurs rapidly during the summer months.

Figure 21 illustrates the simulated nitrogen concentration time-line of the recharge water at the water table (bottom of the simulated domain). Recharge concentrations initially are 50 mg N/l, then decrease to approximately 25 mg/l. This agrees well with results from groundwater monitoring after accounting for the travel time between the field and monitoring wells. Higher concentrations in the winter 2000 are due to large amounts of mineralized (and nitrified) nitrogen leaching to the water table after the main growing season in 1999.

Concentration is not a measure of nitrogen fluxes, whoever. It merely indicates the nitrate concentration at the bottom of the unsaturated zone profile. Actual daily loading rates to groundwater (in units of kg N ha-1 yr-1) are shown in Figure 22. The results indicate that groundwater loading is a very transient event with high spikes associated with high irrigation amounts or significant precipitation events.

–  –  –

Campbell, G.S. 1985. Chapter 12 Atmospheric boundary conditions. p.134-145. In Campbell, G.S. Soil physics with BASIC Transport models for soil- plant systems. Elsevier.

Meisinger, J.J., and G.W. Randall. 1991. Estimating nitrogen budgets for soil-crop systems. In R.F. Follett, D.R. Keeney, and R.M. Cruse (eds.) Managing Nitrogen for Groundwater Quality and Farm Profitability. Soil Sci. Soc. Am. Madison, Wisconsin.

Šim nek, J, M. Sejna, and M.Th. van Genuchten. 1998. The HYDRUS-1D software package for simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media Version 2.0. U.S. Salinity Laboratory: 178p.

UCCE: University of California Cooperative Extension 1987. Leaflet 21427: Using reference evapotranspiration (ET0) and crop coefficients to estimate crop evapotranspiration (ETc) for agronomic crops, grasses, and vegetable crops, University of California.

–  –  –

Irrigation (cm) Figure 6: Calibrated soil water retention curves (three heavy lines) and curves for sand, loamy sand, sandy loam, loam, silt, and silt loam based on HYDRUS-1D database.

–  –  –

–3 0.3 0.3 0.2 0.2

–  –  –

0.4 0.4 –3 –3 0.3 0.3 0.2 0.2 –3 0.3 0.3 0.2 0.2

–  –  –

0.4 0.4 –3 –3 0.3 0.3 0.2 0.2 –3 0.3 0.3 0.2 0.2

–  –  –

0.4 0.4 –3 –3 0.3 0.3 0.2 0.2 Figure 11: Measured and calculated N uptake amounts for various values of the NH4-N isotherm coefficient. N uptakes were calculated from soluble NH4-N and NO3-N in the root zone, which was calculated from the measured NH4-N and NO3-N and the assumed isotherm coefficient, and root water uptake fluxes estimated from water movement simulation.

–  –  –

-2 -2 Figure 12(a): Contour maps of root mean square error between measured and calculated total NH4-N concentrations and NO3-N concentrations in soil (kOrg-N = 0.1).

–  –  –

-2 -2 Fig.12(b): Contour maps of root mean square error between measured and calculated total NH4-N concentrations and NO3-N concentrations in soil (kOrg-N = 1.0).

–  –  –

-2 -2

-2 -2 Figure 12(d): Contour maps of root mean square error between measured and calculated total NH4-N concentrations and NO3-N concentrations in soil (kOrg-N = 100.0).

–  –  –

-2 -2 Figure 12(e): Contour maps of root mean square error between measured and calculated total NH4-N concentrations and NO3-N concentrations in soil (kOrg-N = 1000.0).

–  –  –

Pages:     | 1 |   ...   | 18 | 19 || 21 | 22 |

Similar works:

«1 TESTA BRANCA, O TROPEIRO Alfred Reitz* Aconteceu no altiplano, no oeste de Santa Catarina. Desencilhei meu cavalo diante da pequena venda rural. Ao perguntar ao comerciante, um sírio, onde ficava a entrada do pasto, ele apontou para um imenso portão de varas, logo atrás do puxado. „Qual o tamanho do pasto? Fechado?” „Dez alqueires. quatro arames em todo redor”. Dez alqueires! Uma área imensa. Diabos! Pela manhã bem cedinho provavelmente teria que procurar muito para achar meu...»

«External Capital and the Drivers of Entrepreneurial Success in Large scale Dairying Experiences from the United States and their potential application to the UK David Alvis A 2008 Oxford Farming Conference / Nuffield Farming Scholarship Trust award Thanks and acknowledgements I would first and foremost like to thank my sponsors, the Oxford Farming Conference and the Nuffield Farming Scholarship Trust for giving me the opportunity to undertake this study. It has been an immensely rewarding...»

«Kenya: A Country of Contrasts By Johan Woxenius September 2011 Kenya is defined by its immense contrasts. The country’s natural habitat ranges from sandy beaches and rain forests to savannahs crowded with exotic animals and mountains with glaciers to semi-arid bush land and arid deserts. People are divided into very different tribes, and income levels vary beyond comprehension. Telecommunications are highly developed, whereas Kenya does not boast about its manufacturing sector. Agriculture...»

«Sundaresan School of Animal Husbandry and Dairying SAM HIGGINBOTTOM INSTITUTE OF AGRICULTURE, TECHNOLOGY & SCIENCES (Formerly Allahabad Agriculture Institute) (Deemed to-be-University) Allahabad – 211007 Course Structure of B.Sc. Animal Husbandry & Dairying [now B.Sc. (H) Dairying] SEMESTERI Course code Course title L-T-P Credits CSIT 301 Introduction to Computer Application 2-0-2 3 MAS 303 Elementary Mathematics-I 2-0-0 2 LNG 300 English and Basic Technical writing 3-0-0 3 MBFT 349...»

«18th International Farm Managment Congress Methven, Canterbury, New Zealand IFMA18 – Theme 3 Farm Management CANTERBURY DAIRYING A STUDY IN LAND USE CHANGE AND INCREASING PRODUCTION M.C. Pangborn and K.B.Woodford Agricultural Management Group, Lincoln University, Lincoln 7647 New Zealand Abstract The purpose of this research was to quantify the extent of the increase in dairy farming in Canterbury, New Zealand, and to examine factors that have led to the changes in land use and production. In...»

«Dairy Birthe Lassen Dairy production in South Africa impressions Country Report 2012/1 Dairy Production in South Africa – impressions Acknowledgements My thanks go to the European Dairy Farmers who co-financed the journey to South Africa and the participation at the World Dairy Summit (IDF 2012) in Cape Town. Birthe J. Lassen Dairy farming in a free market: Impressions from South Africa For the quick reader: 2,6 billion kg of milk per year/2,300 dairy farmers/average herd size: 300 cows per...»

<<  HOME   |    CONTACTS
2016 www.theses.xlibx.info - Theses, dissertations, documentation

Materials of this site are available for review, all rights belong to their respective owners.
If you do not agree with the fact that your material is placed on this site, please, email us, we will within 1-2 business days delete him.