scholarly journals The spatial variability of mineral nitrogen content in topsoil and subsoil

2011 ◽  
Vol 50 (No. 10) ◽  
pp. 425-433 ◽  
Author(s):  
J. Haberle ◽  
M. Kroulík ◽  
P. Svoboda ◽  
J. Lipavský ◽  
J. Krejčová ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Strong Dependence ◽  
Length Distribution ◽  
Mineral Nitrogen ◽  
Nitrate Content ◽  
Soil Conditions ◽  
Soil Mineral Nitrogen ◽  
Experimental Field ◽  
Spatial Dependencies

Spatial variability of soil mineral nitrogen N<sub>min </sub>(N-NH<sub>4</sub><sup>+</sup>, N-NO<sub>3</sub><sup>&ndash;</sup>) in a soil profile down to 60 cm was determined in a&nbsp;19-ha experimental field in Prague-Ruzyně for four years. Winter wheat was grown in the years 2000 and 2001, oats in 2002 and mustard in 2003. Root length distribution and depth of the crops were determined at four locations representing different soil conditions within the experimental field. The coefficient of variation as the measure of the variability of nitrate N in topsoil and subsoil (0&ndash;30 and 30&ndash;60 cm, respectively) in the experimental years ranged between 18&ndash;39 and 20&ndash;37%, respectively. It was mostly the same or slightly greater in subsoil than in topsoil in respective years. The variability of ammonium N in topsoil and subsoil ranged between 4&ndash;58 and 11&ndash;27%, respectively. It was similar in topsoil and subsoil, except for autumn 2000. There was a positive relation between nitrate content in top and subsoil on all sampling terms. We did not find a relationship between N<sub>min</sub> contents in experimental years. Spatial dependencies were evaluated on the basis of model variogram parameters. The nugget value expressed as a&nbsp;percentage value of the total variogram&rsquo;s sill was used for the class of spatial dependence determination. When a spatial dependence of the observed factor was found it was within a range of medium-strong dependence. Only in two cases a strong spatial dependence was found. A considerable variability was also found out in the variogram&rsquo;s range, which was between 61 and396 m. All these facts pose a problem for further actions, such as appropriate design of a sampling grid, measured data spatial interpolation and application maps design.

scholarly journals Distribution of Mineral Nitrogen in Soil in Relation to Risk of Nitrate Leaching in Farms with Irrigated Vegetables and Early Potatoes

2018 ◽  
Vol 26 (2) ◽  
pp. 47-54
Author(s):  
Jan Haberle ◽  
Pavel Svoboda ◽  
Tomáš Šimon ◽  
Gabriela Kurešová ◽  
Barbora Henzlová ◽  
...  
Keyword(s):  
Nitrate Leaching ◽  
Soil Layer ◽  
Mineral Nitrogen ◽  
Nitrate Content ◽  
Winter Period ◽  
Vegetable Production ◽  
Soil Mineral Nitrogen ◽  
The Czech Republic ◽  
Water Percolation ◽  
Residual Nitrate

Abstract Vegetable production may be the source of excessive residual nitrate that is prone to leaching to waters. To ascertain the risk of nitrate leaching in water collection area, the content of soil mineral nitrogen (Nmin = N-NO3− + N-NH4+) down to 120 cm depth was monitored in the years 2013–2016 on vegetable farms along lower Jizera river (in the Czech Republic). The risk of nitrate leaching below 30, 60, 90 and 120 cm during winter period was simulated with a simple model. The depths represent the limits of effective root depth and N depletion of groups of vegetables and field crops. The average autumn mineral nitrogen content in the fields, during experimental years, ranged from 101 kg to 134 kg N·ha−1 in the 0–120 cm soil layer, 85 to 92% of which was in the form of nitrate. The calculated leaching of nitrate from the topsoil (0–30 cm) and shallow subsoil (0–60 cm) ranged from 27 to 41%, and from 7 to 14% of autumn content, respectively. The risk of leaching below 60 cm and 90 cm was near to none during the experimental years due to the exceptionally low precipitation. High nitrate content in subsoil layers below 60 cm constitutes risk of leaching and water pollution due to shallow root systems of many vegetables and potatoes in seasons with normal weather and higher water percolation.

Nodulation and early growth of faba bean (Vicia faba L.) and pea (Pisum sativum L.) as affected by strain of Rhizobium, NO3- supply, and growth temperature

1989 ◽  
Vol 40 (5) ◽  
pp. 991 ◽  
Author(s):  
a Herdin ◽  
JH Silsbury
Keyword(s):  
Seedling Growth ◽  
Faba Bean ◽  
Mineral Nitrogen ◽  
Nodule Development ◽  
Soil Conditions ◽  
Soil Mineral Nitrogen ◽  
Nitrogen Concentrations ◽  
Early Seedling ◽  
Better Than ◽  
Late Sowing

The effectiveness of each of two strains of Rhizobium, TA 101 and SU 39 1, on 'Fiord' faba bean and on two cultivars of pea 'A102' and 'Early Dun', was estimated in pot experiments. Plants were grown either without NO3- or with up to 7.5 mM NO3- and at 10, 15, and 20�C to assess the effects of mineral nitrogen and of temperature on plant growth, nodulation, and nitrogen fixation (acetylene reduction).The strains TA 101 and SU 391 were slow to nodulate 'Fiord' faba bean (up to 50 days from sowing) but were rapid (20 days) on pea. TA 101 nodulated the pea cultivar 'A 102' significantly better than it did 'Early Dun', and was generally more effective in supplying N to its host than was SU 391 in the absence of NO3-.Nodule development in 'Fiord' inoculated with SU391 was slightly stimulated in the presence of 2.5 mM NO3-, which compensated for the poor early seedling growth which occurred when plants were grown without mineral nitrogen. Both 5.0 and 7.5 mM NO3- delayed nodulation, decreased nodule number and decreased nodule activity of 'Fiord'. NO3- also had a strongly depressive effect on nodulation and nodule activity of pea but strongly promoted plant growth.Seedling growth and nodulation were poorer at 10�C than at either 15 or 20�C in both faba bean and pea. Nodulation of 'Fiord' was markedly reduced by the low temperature, and it appears likely to be slow in the field when the soil temperature is low (10�C) as may occur after late sowing. The known yield advantage of early planting of this cultivar may in part be due to better nodulation under warm (15�C) soil conditions. It is concluded that strains TA 101 and SU 391 are not very effective on 'Fiord' faba bean, although poor seedling growth due to poor nodulation with these inoculants may be compensated for by soil mineral nitrogen. Concentrations of soil NO3- equivalent to 2.5 to 5.0 mM, however, may depress the capacity of both faba bean and pea to fix nitrogen in the field.

Link between paddy soil mineral nitrogen release and iron and manganese reduction examined in a rice pot growth experiment

Geoderma ◽  
2018 ◽  
Vol 326 ◽  
pp. 9-21 ◽  
Author(s):  
Masuda Akter ◽  
Heleen Deroo ◽  
Eddy De Grave ◽  
Toon Van Alboom ◽  
Mohammed Abdul Kader ◽  
...  
Keyword(s):  
Paddy Soil ◽  
Mineral Nitrogen ◽  
Nitrogen Release ◽  
Growth Experiment ◽  
Soil Mineral ◽  
Soil Mineral Nitrogen ◽  
Manganese Reduction ◽  
Iron And Manganese

Soil mineral nitrogen and nitrate leaching losses in soil tillage systems combined with a catch crop

1999 ◽  
Vol 50 (2) ◽  
pp. 115-125 ◽  
Author(s):  
Maria Stenberg ◽  
Helena Aronsson ◽  
Börje Lindén ◽  
Tomas Rydberg ◽  
Arne Gustafson
Keyword(s):  
Nitrate Leaching ◽  
Mineral Nitrogen ◽  
Soil Tillage ◽  
Catch Crop ◽  
Soil Mineral ◽  
Soil Mineral Nitrogen ◽  
Tillage Systems ◽  
Leaching Losses

scholarly journals Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Gabriel Soropa ◽  
Olton M. Mbisva ◽  
Justice Nyamangara ◽  
Ermson Z. Nyakatawa ◽  
Newton Nyapwere ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Soil Ph ◽  
Strong Evidence ◽  
Spatial Dependence ◽  
Random Component ◽  
Smallholder Farming ◽  
Mineral N ◽  
Spatial Trend ◽  
Spatial Trends

AbstractA study was conducted to examine spatial variability of soil properties related to fertility in maize fields across varying soil types in ward 10 of Hurungwe district, Zimbabwe; a smallholder farming area with sub-humid conditions and high yield potential. Purposively collected and geo-referenced soil samples were analyzed for texture, pH, soil organic carbon (OC), mineral N, bicarbonate P, and exchangeable K. Linear mixed model was used to analyze spatial variation of the data. The model allowed prediction of soil properties at unsampled sites by the empirical best linear unbiased predictor (EBLUP). Evidence for spatial dependence in the random component of the model was evaluated by calculating Akaike’s information criterion. Soil pH ranged from 4.0 to 6.9 and showed a strong spatial trend increasing from north to south, strong evidence for a difference between the home and outfields with homefields significantly higher and between soil textural classes with the sand clay loam fraction generally higher. Soil OC ranged from 0.2 to 2.02% and showed no spatial trend, but there was strong evidence for a difference between home and outfields, with mean soil OC in homefields significantly larger, and between soil textural classes, with soil OC largest in the sandy clay loams. Both soil pH and OC showed evidence for spatial dependence in the random effect, providing a basis for spatial prediction by the EBLUP, which was presented as a map. There were significant spatial trends in mineral N, available P and exchangeable K, all increasing from north to south; significant differences between homefields and outfields (larger concentrations in homefields), and differences between the soil textural classes with larger concentrations in the sandy clay loams. However, there was no evidence for spatial dependence in the random component, so no attempt was made to map these variables. These results show how management (home fields vs outfields), basic soil properties (texture) and other factors emerging as spatial trends influence key soil properties that determine soil fertility in these conditions. This implies that the best management practices may vary spatially, and that site-specific management is a desirable goal in conditions such as those which apply in Ward 10 of Hurungwe district in Zimbabwe.

Effect of Savanna windrow wood burning on the spatial variability of soil properties

2021 ◽  
Vol 51 ◽  
Author(s):  
Diogo Neia Eberhardt ◽  
Robélio Leandro Marchão ◽  
Pedro Rodolfo Siqueira Vendrame ◽  
Marc Corbeels ◽  
Osvaldo Guedes Filho ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Variability ◽  
Spatial Dependence ◽  
Chemical Properties ◽  
Spatial Arrangement ◽  
Nugget Effect ◽  
Tropical Savannas ◽  
Geostatistical Methods ◽  
Exchangeable Ca ◽  
Spherical Models

ABSTRACT Tropical Savannas cover an area of approximately 1.9 billion hectares around the word and are subject to regular fires every 1 to 4 years. This study aimed to evaluate the influence of burning windrow wood from Cerrado (Brazilian Savanna) deforestation on the spatial variability of soil chemical properties, in the field. The data were analysed by using geostatistical methods. The semivariograms for pH(H2O), pH(CaCl2), Ca, Mg and K were calculated according to spherical models, whereas the phosphorus showed a nugget effect. The cross semi-variograms showed correlations between pH(H2O) and pH(CaCl2) with other variables with spatial dependence (exchangeable Ca and Mg and available K). The spatial variability maps for the pH(H2O), pH(CaCl2), Ca, Mg and K concentrations also showed similar patterns of spatial variability, indicating that burning the vegetation after deforestation caused a well-defined spatial arrangement. Even after 20 years of use with agriculture, the spatial distribution of pH(H2O), pH(CaCl2), Ca, Mg and available K was affected by the wood windrow burning that took place during the initial deforestation.

Spatial dynamics of soil moisture and temperature in a black spruce boreal chronosequence

2006 ◽  
Vol 36 (11) ◽  
pp. 2794-2802 ◽  
Author(s):  
Ben Bond-Lamberty ◽  
Karen M Brown ◽  
Carol Goranson ◽  
Stith T Gower
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Sampling Design ◽  
Black Spruce ◽  
Structural Characteristics ◽  
Spatial Dynamics ◽  
Spherical Model ◽  
Local Scale ◽  
Spatial Dependencies ◽  
The Mean

This study analyzed the spatial dependencies of soil moisture and temperature in a six-stand chronosequence of boreal black spruce (Picea mariana (Mill.) BSP) stands. Spatial variability of soil temperature (TSOIL) was evaluated twice during the growing season using four transects in each stand, employing a cyclic sampling design with measurements spaced 2–92 m apart. Soil moisture (θg) was measured on one occasion. A spherical model was used to analyze the geostatistical correlation structure; θg and TSOIL at the 7- and 21-year-old stands did not exhibit stable ranges or sills. The fits with stable ranges and sills modeled the spatial patterns in the older stands reasonably well, although unexplained variability was high. Calculated ranges varied from 3 to 150 m for these stands, lengths probably related to structural characteristics influential in local-scale energy transfer. Transect-to-transect variability was significant and typically 5%–15% of the mean for TSOIL and 10%–70% for θg. TSOIL and θg were negatively correlated for most stands and depths, with TSOIL dropping 0.5–0.9 °C for every 1% rise in θg. The results reported here provide initial data to assess the spatial variability of TSOIL and θg in a variety of boreal forest stand ages.

scholarly journals GEOSTATISTICAL MODELING OF TIMBER VOLUME SPATIAL VARIABILITY FOR Tectona grandis L. F. PRECISION FORESTRY

CERNE ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Allan Libanio Pelissari ◽  
Marcelo Roveda ◽  
Sidney Fernando Caldeira ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Tectona Grandis ◽  
Wood Products ◽  
Geostatistical Modeling ◽  
Timber Volume ◽  
Silvicultural Treatments ◽  
Punctual Kriging ◽  
Precision Forestry ◽  
Taper Models

ABSTRACT Considering the hypothesis that the wood volumes present spatial dependence, whose knowledge contributes for the precision forestry, the aim of this work was to estimate the volume spatial variability for timber assortments and identify their spatial patterns on Tectona grandis stands. A dataset of 1,038 trees was used to fit taper models and estimate the total stem, sawlog, and firewood volumes in 273 plots allocated on T. grandis stands at eight years old, which represents the second thinning that enables commercial volumes. Semivariograms models was applied to fit the spatial dependence, and punctual kriging was used to compose volume maps. Geostatistical modeling allowed us to estimate the T. grandis spatial variability and develop timber volume maps. Thus, silvicultural treatments, such as thinning and pruning, as well as for planning spatial interventions, are possible to be recommended for aimed wood products.

scholarly journals Spatial variability of soil chemical properties after coffee tree removal

2009 ◽  
Vol 33 (5) ◽  
pp. 1507-1514 ◽  
Author(s):  
Sidney Rosa Vieira ◽  
Osvaldo Guedes Filho ◽  
Márcio Koiti Chiba ◽  
Heitor Cantarella
Keyword(s):  
Spatial Variability ◽  
Spatial Dependence ◽  
Crop Yields ◽  
Management Strategies ◽  
Chemical Properties ◽  
Spherical Model ◽  
Soil Chemical Properties ◽  
Kriging Interpolation ◽  
Geostatistical Methods ◽  
Plant Removal

Assessing the spatial variability of soil chemical properties has become an important aspect of soil management strategies with a view to higher crop yields with minimal environmental degradation. This study was carried out at the Centro Experimental of the Instituto Agronomico, in Campinas, São Paulo, Brazil. The aim was to characterize the spatial variability of chemical properties of a Rhodic Hapludox on a recently bulldozer-cleaned area after over 30 years of coffee cultivation. Soil samples were collected in a 20 x 20 m grid with 36 sampling points across a 1 ha area in the layers 0.0-0.2 and 0.2-0.4 m to measure the following chemical properties: pH, organic matter, K+, P, Ca2+, Mg2+, potential acidity, NH4-N, and NO3-N. Descriptive statistics were applied to assess the central tendency and dispersion moments. Geostatistical methods were applied to evaluate and to model the spatial variability of variables by calculating semivariograms and kriging interpolation. Spatial dependence patterns defined by spherical model adjusted semivariograms were made for all cited soil properties. Moderate to strong degrees of spatial dependence were found between 31 and 60 m. It was still possible to map soil spatial variability properties in the layers 0-20 cm and 20-40 cm after plant removal with bulldozers.

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