Analysis of spatial variability of physico-chemical properties of soil by hrn94632

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									        Analysis of spatial variability of physico-chemical properties of soil
                  and their potential use in Precision Agriculture

 Bogusław Usowicz, Mieczysław Hajnos, Zofia Sokołowska, Grzegorz Józefaciuk
                    Grzegorz Bowanko, Jan Kossowski

               Institute of Agrophysics, Polish Academy Sciences
     ul. Doświadczalna 4, 20-290 Lublin, http://www.ipan.lublin.pl/~usowicz/

    Spatial variability of plant yield observed under natural conditions is a result of
the spatial variability of soil and meteorological conditions. Spatial variability of
soil can result from a natural variability of soil properties as well as from human
activity. Both above components of variability are in many cases difficult to
distinguish and so these are analyzed simultaneously. Soil texture and mineral
composition as well as their spatial distributions are not significantly dependent on
time. However some soil components having important effect on plant growth and
development, characterized by marked variability not only in time but in space,
may be regulated in field conditions. By improving of plant growth conditions one
tends to optimize the yield from every field site involving minimum costs and
causing minimum hazard for the environment. Recognizing the spatial and time
variability of particular soil features together with an experience coming from a
large number of soil experiments on the effect of these components on yield may
be helpful for decisions on application of particular management practices on
defined soil sites.
    Determination of soil spatial variability needs an adequate number of samples
and application of spatial analysis methods. The number of sampling sites may be
roughly determined from statistical analysis; however the optimal sample number
and their localities needs additional geostatistic analysis that additionally allows to
minimize costs of investigations.
    The aim of this work was to determine spatial variability of selected
physicochemical soil properties important for plant growth, on agricultural fields of
different areas thus collecting data usable for precision agriculture.

          EXPERIMENTAL OBJECT AND METHODS OF ANALYSIS

    The present paper shows results measured in surface layers (1-10 cm) of two
cultivated fields located in Trzebieszów Commune (Lublin voivodship). The first
field (A) had dimensions of 30 × 600 m, and the second one (B) of 50 × 200 m.
The data discussed include: granulometric composition, pH, amount of organic
matter and cation exchange capacity (CEC) all measured using standard soil
science methods and soil moisture measured with TDR just after harvest.
Experimental points were located in these fields in regular networks determined
using a belt-meter and the coordinates of the selected reper points (with a precision
of 1 to 5 m) were estimated using the Trimble's GPS GeoExplorer 3.
    Except of soil properties the spatial distribution of yield was analyzed: mixture
of oat, wheat and barley (on field A), and rye (on field B). The yield distribution
was determined basing on yield coming from 1 m2 squares that covered the given
field in as much as possible uniform network. For field A 72 squares were selected
and for field B 30 squares. The plants were harvested manually, threshed and the
grains dried and weighted.
    Separately for both objects basic statistical parameters of the studied soil
properties were calculated, including the average value, standard deviation,
coefficient of variability (CV), maximum and minimum values, as well as
estimates of the distribution of a given property as skewness and kurtosis. Spatial
characteristics of the experimental data were performed using geostatistical
methods. Spatial variability of each soil property on a given field was determined
using semivariograms. Value of nugget, ranges and thresholds of spatial
autocorrelations were determined and the semivarigram models were fitted to the
empirical data along with estimation of the model fitting parameters. The statistics,
semivariograms, estimates of the studied soil properties and mapping was
performed using computer programs GeoEas, Statistica 6, Variowin 2.21, GS+5
Demo and Surfer 8 Demo.

                                     RESULTS

    Te average yield of oat-wheat-barley mixture from 1 m2 square on the field A
(188 g) was over 100 g lower than the average yield of rye in field B (302 g),
similarly as the range of the yield being over 100 g lower for the mixture. The yield
of rye was between 117 to 556 g·m–2 and of the mixture from 55 to 325 g·m–2. The
coefficient of variability for the yield was similar for both fields (23-28%). The
distribution of the yield of the mixture was smoother than that for the rye.
    The content of particular granulometric fractions was markedly different in the
field-scale. In the surface layer (0-10 cm) of the field A the sand fraction accounted
for 48.8%, and on the field B 54.7%. The silt fraction was 34.5% (field B) and
37.2% (field A) and the clay fraction was 10.8% (field B) and 14% (field A). The
organic matter content was small for both fields, around 0.8%. The minimum and
the maximum content of organic matter was 0.014 and 1.8%, respectively. The soil
was acidic with the average pH (H2O) value of 4.46 for the field A and 4.81 for the
field B. Minimum pH for all samples was 3.93 and the maximum 6.59.
    The average CEC value was around 11 cmol·kg–1, however high differences
were noted for particular samples, from 3.69 to 23.8 cmol·kg–1.
    The average value of soil moisture differ less than 1% for both fields (0.14-0.15
m3 m-3). Also the variability coefficients were similar, around 35% for both fields.
    The investigated soil properties exhibited space variability, mostly of a spherical
character. The exponential character was observed only for three variables (sand
content in field A and silt and organic matter content in field B). Parameters of
semivariograms showed that nugget effect is present which indicates less
variability of the investigated properties in comparison to the assumed minimum
distance of sampling.
    Values of sill are similar to the values of variance estimated using standard
methods. From analysis of the above values one can suspect that within the studied
fields no evident trends of changes in the studied soil properties occur.
    Values of sill were governed by the content of particular granulometric
fractions with the highest values noted for sand, smaller for silt, and the smallest
for clay and these were markedly higher for field A than B. Values of sill for other
soil properties were also higher for field A.
    Spatial dependencies of physicochemical soil properties seemed to be related to
the magnitude of the studied field. For field A the range was from 78 to 500 m and
for field B - 12 to 310 m.
    The range of spatial dependence for yield was similar for both fields and equal
to around 27 m, however for the field B of lower area the semivariance was about
1.8 times higher than in field A with mixed crop.
    The spatial dependence of soil moisture for both studied fields was recognized.
For field A the exponential dependence occurred whereas spherical one for field B.
The ranges of spatial dependencies were similar, around 100 m for both fields.
    Parameters and models of semivariograms estimated for particular soil
properties and yield, as well as the data measured in particular points were applied
– using kriging method – for mapping of the spatial distributions for given object
(Figs 1 and 2) and for calculating the estimation error.
    The estimation errors for all the studied data did not exceeded 10% of the
analyzed property. In the vicinity of the experimental points these errors were
markedly lower (around 1-2%) and the highest values occurred on the boundaries
of sampling networks.
    The yield distribution was more or less related to all investigated soil physical
and chemical properties. Thus knowing the spatial distributions of physicochemical
soil properties and their influence on plants, one may predict the approximate
amount of yield in a given location on particular field. Simultaneously, the
estimated maps of spatial distribution of soil physicochemical properties provide
essential information which can be used for making a proper decision on
agricultural management practices on the whole field or on its part.
    Marked differentiation of spatial distributions of all investigated parameters
observed on both fields despite of their small areas certifies that having larger
amount of sampling points is advantageous. Visualization of soil physical and
chemical properties and their estimation errors allows for distinguishing areas on a
given field wherein an increase (or a decrease) of the amount of the sampling
points is required in further measurements to optimize the error values and adjust
them to the experimenter needs.
                                                                      Plon Yield [g]
                a)
Distance [m]
Odległość




                              Odległość Distance [m]



                b)                                                    pH [H2O]
Distance [m]
Odległość




                               Odległość Distance [m]



                c)                                              CEC [cmol kg-1]
Distance [m]
Odległość




                               Odległość Distance [m]



                d)                                              Wilgotność
Distance (m)




                                                           Water content (m3 m-3)
Odległość




                                Odległość - Distance (m)



Fig. 1. Spatial distribution of grain yield (a), pH (b), CEC (c) and water content (d)
in the cultivated field A.
                a)                                                         Plon Yield [g]
Distance [m]
Odległość




                               Odległość Distance [m]
                b)                                                           pH [H2O]
Distance [m]
Odległość




                               Odległość Distance [m]
                c)
                                                                         CEC [cmol kg-1]
Distance [m]
Odległość




                               Odległość Distance [m]
                                                                    Wilgotność
                d)                                             Water content (m3 m-3)
Distance (m)
Odległość




                               Odległość - Distance (m)

Fig. 2. Spatial distribution of grain yield (a), pH (b), CEC (c) and water content (d)
in the cultivated field B.
                                   SUMMARY

    The studies performed may have not only theoretical but also practical
importance. The general state of the basic soil chemical and physical properties in
the variously used fields was recognized, the parameters describing their spatial
variability were determined and the spatial dependences of soil features was
mapped. The knowledge on spatial variability of soil properties and plant yield in
agricultural fields allowed for estimation of the realistic conditions of plant
development that may constitute a base for selection of the areas requesting
changes in agricultural measures as liming or fertilization, as well as may serve for
elaboration of detailed agrotechnical instructions for precise soil management.
    The studies described herein constitute a basis for precision agriculture that
actually under increasing interest in developed countries.
    Particular attention should be placed on these soil features which are rather
stable in time but rather difficult in estimation. However dynamic soil features,
changing with external conditions are also useful and these should be collected in
databases, as well.

								
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