**1. Introduction**

96 Agricultural Science

Chen, P., Haboudane, D., Trembaly, N., Wang, J., Vigneault, P., Li, B. (2010). New spectral

Haboudane, D., Miller, J.R., Trembaly, N., Zarco-Tejada, P.J., Dextraze, L. (2002). Integrated

Hatfield, J.L., Jaynes, D.B., Burkart, M.R., Cambardella, C.A., Moorman, T.B., Prueger, J.H.,

Hatfield, J.L., Prueger, J.H., Kustas, W.P. (2007). Spatial and temporal variation of energy and carbon dioxide fluxes in corn and soybean fields in central Iowa. *Agron. J.* 99:285-296. Hatfield, J.L., Gitelson, A.A., Schepers, J.S., and Walthall, C.L. (2008). Application of Spectral Remote Sensing for Agronomic Decisions. *Agron. J.* 100:S-117-S-131. Hatfield, J.L., McMullen, L.D., Jones, C.W. (2009). Nitrate-nitrogen patterns in the Raccoon

Hernandez-Ramirez, G., Hatfield, J.L., Prueger, J.H., and Sauer, T.J. (2010). Energy balance

Jaynes, D.B., Colvin. T.S. (1997). Spatiotemporal variability of corn and soybean yield. *Agron.* 

Jaynes, D.B., Hatfield, J.L., Meek, D.W. (1999). Water Quality in Walnut Creek Watershed:

Jaynes, D.B, Colvin, T.S., Karlen, D.L., Cambardella, C.A., Meek, D.W. (2001). Nitrate loss in

Inman, D., Khosla, R., Reich, R., Westfall, D.G. (2008). Normalized difference vegetation index and soil color-based management zones in irrigated maize. *Agron. J.* 100:60-66. Lee, Y., Yang, C., Chang, K., Shen, Y. (2008). A simple spectral index using reflectance of 735

Massey, R.E., Myers, D.B., Kitchen, N.R., Sudduth, K.A. (2008). Profitability maps as an input for site-specific management decision making. *Agron. J.* 100:52-59. Ritchie, J.T., Burnett. E. (1971). Dryland evaporative flux in a subhumid climate: II. Plant

Sadler, E.J., Bauer, P.J., Busscher, W.J. (2000a). Site-specific analysis of a droughted corn

Sadler, E.J., Bauer, P.J., Busscher, W.J., Miller, J.A. (2000b). Site-specific analysis of a

Schepers, J.S., Francis, D.D., Vigil, M., Below, F.E. (1992). Comparison of corn leaf nitrogen

reflectance measurements of corn leaves from plants with different nitrogen and

concentration and chlorophyll meter readings. *Commun. Soil Sci. Plant Anal.*

droughted corn crop: II. Water use and stress. *Agron. J.* 92:403-410. SAS Institute (2009) User's guide. Statistics. Version 9, SAS Institute, Cary, North Carolina. Schepers, J.S., Blackmer, T.M., Wilhelm, W.W., Resende, M. (1996). Transmittance and

Herbicides and Nitrate in Surface Waters. *J. Environ. Qual.* 28:45-59.

nm to assess nitrogen status of rice canopy. *Agron. J.* 100:205-212.

crop: I. Growth and grain yield. *Agron. J.* 92:395-402.

water supply. *J. Plant Physiol.* 148:523-529.

application to precision agriculture. *Remote Sens. Environ.* 81:416-426. Hatfield, J.L., Prueger, J.H. (2001). Increasing nitrogen use efficiency of corn in Midwestern

Science and Policy. *TheScientificWorld* (2001) 1(S2):682-690.

*Remote Sens. Environ.* 114:1987-1997.

practices. *J. Environ. Qual.* 28:11-24.

*Theor. Appl. Climatol.* 100:79-92.

influences. *Agron. J.* 63:56-62.

23:2173-2187.

*J.* 89:30-37.

indicator assessing the efficiency of crop nitrogen treatment in corn and wheat.

narrow-band vegetation indices for prediction of crop chlorophyll content for

cropping systems. Proceedings of the 2nd International Nitrogen Conference on

Smith, M.A. (1999). Water quality in Walnut Creek watershed: Setting and farming

River Basin as related to agricultural practices. *J. Soil and Water Conserv.* 64:190-199.

and turbulent flux partitioning in a corn-soybean rotation in the Midwestern U.S.

subsurface drainage as affected by nitrogen fertilizer rate. *J. Environ. Qual.* 30:1305-1314.

Long-term experiments are very important in studying the changes of soil fertility and environmental conditions as well as in analyzing the stability and quality of crop production. Such experiments give us more information how to use the good agronomic practices and how to protect the nature. Probably the oldest still-running arable crop fertilizer experiment is the Broadbalk Experiment established by John B. Lawes in Rothamsted (UK) in 1843 (Goulding et al., 2000). Thanks to this experiment many other long-term fertilizer experiments were established worldwide (Sims, 2006; Khan et al., 2007; Takahashi&Anwar, 2007; Kunzova&Hejcman, 2009).

In Bulgaria also have investigations on such long-term fertilizer trails (Koteva, 2010; Panayotova, 2005; Nankova et al., 1994 & 2005; Nankova, 2010).

Dobrudzha Agricultural Institute-General Toshevo is situated in North-Eastern part of Bulgaria on black earth zone (Picture 1). The main soil type is chernozem (Haplic Chernozems WRBSS, 2006).

The aim of this investigation was to follow the effect of the long-term agronomy practices and especially fertilization on the nutrition regime of slightly leached chernozem soil in the region of South Dobrudzha after 40 years mineral fertilization with different norm and combination between nitrogen, phosphorus and potassium.

A long-term fertilizer experiment , which was established in 1967 is still running. In two field crop rotation (wheat-maize) four nitrogen and phosphorus and three potassium norms were tested – 0, 60, 120, 180 and 0, 60, 120 kg/ha respectively. The experiment was designed according to the method of the "net square", applying the full version of the design in four replications. The experiment was designed by the method of the "net square", applying the full version of the design (4 x 4 x 3 = 48) in four replications. On the 40th year from the beginning of the trial (2007) after wheat harvest, soil samples were taken every 20 cm down the soil profile till depth 400 cm. A motor-driven portable soil sampler was used (Iliev&Nankova, 1994; Iliev, 2000). The changes of some agrochemical characteristics were determined in selected variants with high average 40th year productivity.

Long-Term Mineral Fertilization and Soil Fertility 99

The soil acidity forms, averaged for the investigated depth of the 0-400 cm profile, were significantly affected by the type of fertilizer combination. The independent effect of the factor mineral fertilization was higher on exchangeable Al3+, Ca2+ and the sum of Са2+ and Mg2+, and significantly lower - on the values of residual hydrolytic acidity and the rate of alkali saturation. The depth of the investigated profile was the factor with decisive effect on all forms of soil acidity. Its effect on the pH values, the residual hydrolytic acidity and the alkali saturation degree was over 90 %. Significantly lower was its influence on the

Ca

20,3

7,2

Fert.variants (A) Depth (B) Fert.variants \* Depth

72,5

Al

70,6

7,0

Fert.variants (A) Depth (B) Fert.variants \* Depth

22,5

exchangeable Al3+ (22.5%) and the sum of exchangeable Са and Mg (45.8%).

Ca+Mg

Fert.variants (A) Depth (B) Fert.variants \* Depth

7,6

45,8

In spite of the maximum degree of significance of the effect of mineral fertilization on the forms of soil acidity, the amplitude of variation of the separate indices was not so well expressed as in the separate soil layers up to 400 cm down the soil profile. Averaged for the fertilization variants, pH varied from 6.35 (10-20 cm) to 8.53 (260 – 300 cm). Soil reaction increased down the soil profile and at the 4th meter there was well expressed correlation between the soil layers forming it. It, however, showed similarities to layers 160-180, 180-200 and 200-220 cm. The layers from 220 to 300 cm possessed higher pH values in comparison to

The amount of exchangeable Ca2+ showed a gradual tendency toward decreasing down the depth profile. Amplitude of variation was from 28.49 cmolckg-1 (60-80 cm) to 18.79 cmolckg-1 (380-400 cm). The surface layers 0-10 and 10-20 cm had lower content of exchangeable Ca2+ in comparison to the layers under them up to depth of 100 cm, being more similar to the amounts found in the 2nd meter. Highest amounts were detected in layers 60-80 cm and 80-100 cm.

The amount of exchangeable Mg2+ had a clear tendency toward increasing down the soil profile, being highest in the 340-360 cm layer (8.10 cmolckg-1). In the trial field, layers 80-100, 120-140 and 60-80 cm had lowest content of exchangeable Mg2+ – about 1-2 cmolckg-1. The surface layers within the 1st meter were comparatively richer in it, but their content

The sum of the two exchangeable cations down the profile varied from 25.38 cmolckg-1 (120- 140 cm) to 30.51 cmolckg-1 (60-80 cm). The surface layers (0-10 cm and 10-20 cm) had lower sorption capacity, ∑Ca+Mg and degree of saturation with bases than the 0-20 cm layer according to the trial beginnig. According to Nankova (2005, Personal Communication) at the start of this long-term experiment the values of these parameters were 34,44 cmolckg-1 , 30,80 cmolckg-1 and 91,2% respectively. Further down the profile the sorption capacity

considerably conceded to the content in the deeper layers of the 3rd and 4th meter.

**2.1 Changes in soil acidity forms** 

pH – H2O 2,0

4,7

93,3

Fig. 1. Power of factors influence

Fert.variants (A) Depth (B) Fert.variants \* Depth

the layers of the 4th meter.

**2.1.1 Soil acidity forms for the 0-400 cm profile** 

46,6

Picture 1. Position of Dobrudzha Agriculture Institute on Bulgaria map (43о 40' northen latitude and 28о 10' eastern longitude)

The soil acidity forms were determined by Ganev&Arsova (1980).

The potential nitrogen-supplying ability of soil was determined through incubation under constant temperature of 30o C at 60 % humidity from its total moisture absorption capacity in order to develop optimal conditions for nitrification. Incubation was done in thermostate to investigate its dynamics at the 14th, 28th and 56th day. The samples were analyzed to determine the amount of nitrate nitrogen in 1 % K2SO4 extract. The ability of NO3-N to form intensive yellow coloration when interacting with disulphurphenoloc acid [C6H3OH(HSO3)2] in alkali media was used.

Carbon contend was valuated using the Tyurin modification (oxidizing with K2Cr2O7/H2SO4 solution in thermostate at 1250С, 45 min, at presence of Ag2SO4 and titration with (NH4)2SO4.FeSO4.6 H2O (Kononova&Belchikova,1961; Spiege at al, 2007; Hegymegi at al, 2007). Composition of soil organic matter was determined by Konnova (1963) and Filcheva&Tsadilas (2002).

Data were analysed with Excel and SPSS 16.0 (2007) and means separated by the Waller-Duncan test (P<0,05).
