Nitrogen Mineralization Variability at Field Using ...€¦ · Nitrogen Mineralization Variability...
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Nitrogen Mineralization Variability at Field Using Vegetation Spectral Indices Zubillaga, Mercedes 1 , Esteban Mosso 1 & Matías Redel 1 Cátedra de Fertilidad y Fertilizantes, Facultad de Agronomía, Universidad de Buenos Aires 1 , Av.San Martin 4453 C1417DSE CABA,Argentina. E:mail: [email protected] INTRODUCTION AND OBJETIVES MATERIALS AND METHODS RESULTS CONCLUSIONS Remote sensing techniques were used to detect the nitrogen mineralization spatial variability at field scale. The aims of this study were 1) to explore the N mineralization variability within a field in the Central Western Pampas, and 2) to determine if N mineralization parameters were related to vegetation indices derived from spectral remote sensing. Spatial variability in N mineralization, even within a small field, represents a potential problem in estimating the quantity of N mineralized under field conditions. The techniques to determine soil N mineralization are time consuming and could be economically prohibitive for precision farming which requires quick estimates of soil nitrogen. This work was funded by UBACyT 20020100100757 Prior to maize sowing, the field was sampled in a grid pattern (5 rows and 10 columns) where soil samples were taken. A long-term aerobic incubation was carried out with these samples to measure potentially mineralizable N. The data of cumulative net N mineralized with time (Nt) were fit to Nt= N0.(1-e-k t), where t is time and N0 is the N mineralization potential. A N balance study was conducted in the field at each of the grid sites for estimating the apparent mineralized N (Nap). Nap is calculated as the difference between the plant N accumulation and the variation soil inorganic nitrogen at the beginning and end of the growing season period. Spectral indices (NVDI, REIP and TCARI) were taken at crop early stages (V6 and V10). In the Central Western Pampas, it is necessary to consider the variability in N mineralization to properly estimate rates of N fertilization. Spectral indices have improved the prediction of nitrogen mineralization within the field. The results are promising because have demonstrated that expensive soil measurements combined with secondary information, such as remotely sensed (spectral) data, and geostatistical techniques were adequate to map N mineralization at field scale. Classical statistics and geostatistics were used to analyze the data. Correlation matrices were calculated among vegetation spectral indices and mineralization parameters. SE pred.= 41 R 2 = 0.53 SE pred= 21 R 2 = 0.88 SE pred.= 17 R 2 = 0.92 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Actual Nap (kg N ha -1 ) Estimated Nap (kg N ha -1 ) Nap (Nap+NVDI6) (Nap+NVDI10) SE pred.= 45 R 2 = 0.46 SE pred= 24 R 2 = 0.85 SE pred.= 20 R 2 = 0.88 0 50 100 150 200 250 300 350 400 450 0 50 100 150 200 250 300 350 400 450 Actual N0 (kg N ha -1 ) Estimated N0 (kg N ha -1 ) Nap (Nap+NVDI6) (Nap+NVDI10) SE pred.= 45 R 2 = 0.46 SE pred= 23 R 2 = 0.85 SE pred.= 21 R 2 = 0.88 0 50 100 150 200 250 300 350 400 450 0 50 100 150 200 250 300 350 400 450 Actual N0 (kg N ha -1 ) Estimated N0 (kg N ha -1 ) N0 N0+TCARI10 N0+REIP10 SE pred.= 21 R 2 = 0.88 SE pred= 20 R 2 = 0.88 SE pred.= 17 R 2 = 0.92 0 50 100 150 200 250 300 0 50 100 150 200 250 300 Actual Nap (kg N ha -1 ) Estimated Nap (kg N ha -1 ) Nap+TCARI6 (Nap+REIP10) (Nap+TCARI10) The errors in the N mineralization predictions at field scale were lower using spectral indices as covariate. These results are shown for Nap (Figures 2 and 3) and N0 (Figures 4 and 5). Table 1. Pearson correlation coefficients for N mineralization and spectral indices Distribution of Nap at field scale Distribution of N0 at field scale Figure 2 and 3: Cross Validation (kriging and cokriging) for Nap. Figure 4 and 5: Cross Validation (kriging and cokriging) for N0. At field scale the N mineralization showed a great spatial variability (Fig 1). Both of the variables were explained by the spectral indices of early crop stages (Table 1). 0 20 40 60 80 100 0 100 200 300 400 500 kg N ha -1 Cumulutive Frequency (%) Nap N0 Table 2 Parameters of the cross semivariograms models for spectral indices with Nap and N0 as a covariate There were direct spatial correlations between the N mineralization and spectral indices. The spherical model was the best fit to the data of cross semivariograms. Almost all the variability had spatial structure and the range varied from 183 to 300 m (Table 2). The semivariograms had strong spatial dependence for Nap and N0, so the spatial distribution of the N mineralization at field scale were estimated by krigging. Spectral Indices Crop Stages V6 V10 NVDI 0.41** 0.64 ** REIP 0.24 0.63 ** N0 TCARI 0.14 0.43 ** NVDI 0.69 ** 0.75 ** REIP 0.20 0.77 ** Nap TCARI 0.44 ** 0.46 ** Nugget Sill Range (m) R 2 0.001 2.931 310 0.66 0.18 7.77 206 0.78 4.20 291.7 183 0.71 N0 NVDI6 NVDI10 REIP10 TCARI10 0.001 2.002 208 0.86 0.01 4.039 237 0.89 0.01 8.04 241 0.86 0.01 2.02 280 0.91 Nap NVDI6 NVDI10 TCARI6 REIP10 1.00 414 284 0.78 Figure 1 Indices Formula NDVI (R 800 -R 670 )/(R 800 +R 670 ) TCARI 3(R 700 -R 670 )-0.2(R 700 -R 550 )(R 700 /R 670 ) REIP 700+40((R 670 +R 780 )/2-R 710 )/(R 730 -R 710 )
Transcript of Nitrogen Mineralization Variability at Field Using ...€¦ · Nitrogen Mineralization Variability...
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Nitrogen Mineralization Variability at Field Using Vegetation
Spectral IndicesZubillaga, Mercedes1, Esteban Mosso1 & Matías Redel1
Cátedra de Fertilidad y Fertilizantes, Facultad de Agronomía, Universidad de Buenos Aires1, Av.San Martin 4453 C1417DSE
CABA,Argentina. E:mail: [email protected]
INTRODUCTION AND OBJETIVES
MATERIALS AND METHODS
RESULTS
CONCLUSIONS
Remote sensing techniques were used to detect the nitrogen
mineralization spatial variability at field scale. The aims of this study were 1)
to explore the N mineralization variability within a field in the Central
Western Pampas, and 2) to determine if N mineralization parameters were
related to vegetation indices derived from spectral remote sensing.
Spatial variability in N mineralization, even within a small field, represents a
potential problem in estimating the quantity of N mineralized under field
conditions. The techniques to determine soil N mineralization are time
consuming and could be economically prohibitive for precision farming whichrequires quick estimates of soil nitrogen.
This work was funded by UBACyT 20020100100757
Prior to maize sowing, the field was sampled in a gridpattern (5 rows and 10 columns) where soil samples were taken.
A long-term aerobic incubation was carried out with thesesamples to measure potentially mineralizable N. The data ofcumulative net N mineralized with time (Nt) were fit to Nt=N0.(1-e-k t), where t is time and N0 is the N mineralizationpotential.
A N balance study was conducted in the field at each ofthe grid sites for estimating the apparent mineralized N (Nap).Nap is calculated as the difference between the plant Naccumulation and the variation soil inorganic nitrogen at thebeginning and end of the growing season period.
Spectral indices (NVDI, REIP and TCARI) were
taken at crop early stages (V6 and V10).
In the Central Western Pampas, it is necessary to consider the variability in N mineralization to properly estimate rates of N fertilization. Spectral indices have improved theprediction of nitrogen mineralization within the field. The results are promising because have demonstrated that expensive soil measurements combined with secondaryinformation, such as remotely sensed (spectral) data, and geostatistical techniques were adequate to map N mineralization at field scale.
Classical statistics and geostatistics wereused to analyze the data. Correlationmatrices were calculated amongvegetation spectral indices andmineralization parameters.
SE pred.= 41
R2
= 0.53
SE pred= 21
R2
= 0.88
SE pred.= 17
R2
= 0.92
0
50
100
150
200
250
300
0 50 100 150 200 250 300
Actual Nap (kg N ha-1
)
Estim
ated N
ap (kg N
ha
-1
)
Nap
(Nap+NVDI6)
(Nap+NVDI10)
SE pred.= 45
R2
= 0.46
SE pred= 24
R2
= 0.85
SE pred.= 20
R2
= 0.88
0
50
100
150
200
250
300
350
400
450
0 50 100 150 200 250 300 350 400 450
Actual N0 (kg N ha-1
)
Estim
ated N
0 (kg N
ha
-1
)
Nap
(Nap+NVDI6)
(Nap+NVDI10)
SE pred.= 45
R2
= 0.46
SE pred= 23
R2
= 0.85
SE pred.= 21
R2
= 0.88
0
50
100
150
200
250
300
350
400
450
0 50 100 150 200 250 300 350 400 450
Actual N0 (kg N ha-1
)
Estim
ated N
0 (kg N
ha
-1
)
N0
N0+TCARI10
N0+REIP10
SE pred.= 21
R2
= 0.88
SE pred= 20
R2
= 0.88
SE pred.= 17
R2
= 0.92
0
50
100
150
200
250
300
0 50 100 150 200 250 300
Actual Nap (kg N ha-1
)
Estim
ated N
ap (kg N
ha
-1
)
Nap+TCARI6
(Nap+REIP10)
(Nap+TCARI10)
The errors in the Nmineralization predictions atfield scale were lower usingspectral indices ascovariate. These results areshown for Nap (Figures 2and 3) and N0 (Figures 4and 5).
Table 1. Pearson correlation
coefficients for N mineralization and
spectral indices
Distribution of Nap at field scale Distribution of N0 at field scale
Figure 2 and 3: Cross Validation (kriging and cokriging) for Nap. Figure 4 and 5: Cross Validation (kriging and cokriging) for N0.
At field scale the N mineralization showed a greatspatial variability (Fig 1). Both of the variables wereexplained by the spectral indices of early crop stages(Table 1).
0
20
40
60
80
100
0 100 200 300 400 500
kg N ha-1
Cu
mu
luti
ve
Fre
qu
en
cy
(%
)
Nap
N0
Table 2 Parameters of the cross
semivariograms models for spectral indices
with Nap and N0 as a covariate
There were direct spatial
correlations between the
N mineralization and
spectral indices. The
spherical model was the
best fit to the data of
cross semivariograms.
Almost all the variability
had spatial structure and
the range varied from
183 to 300 m (Table 2).
The semivariograms hadstrong spatial dependencefor Nap and N0, so thespatial distribution of the Nmineralization at field scalewere estimated by krigging.
Spectral
Indices
Crop Stages
V6 V10
NVDI 0.41** 0.64 **
REIP 0.24 0.63 **
N0
TCARI 0.14 0.43 **
NVDI 0.69 ** 0.75 **
REIP 0.20 0.77 **
Nap
TCARI 0.44 ** 0.46 **
Nugget Sill Range
(m)
R2
0.001 2.931 310 0.66
0.18 7.77 206 0.78
4.20 291.7 183 0.71
N0
NVDI6
NVDI10
REIP10
TCARI10 0.001 2.002 208 0.86
0.01 4.039 237 0.89
0.01 8.04 241 0.86
0.01 2.02 280 0.91
Nap
NVDI6
NVDI10
TCARI6
REIP10 1.00 414 284 0.78
Figure 1
Indices Formula NDVI (R800-R670)/(R800+R670)
TCARI 3(R700-R670)-0.2(R700-R550)(R700/R670)
REIP 700+40((R670+R780)/2-R710)/(R730-R710)
