Comparative study of fertilization effect on weed biodiversity of long term experiments with near field remote sensing methods
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Journal of Plant Diseases and Protection Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz Sonderheft XX, 801-807 (2006), ISSN 1861-4051 © Eugen Ulmer KG, Stuttgart Comparative study of fertilization effect on weed biodiversity of long term experiments with near field remote sensing methods É. LEHOCZKY1, J. TAMÁS2, A. KISMÁNYOKY1, P. BURAI2* 1 University of Veszprém, Georgikon Faculty of Agriculture Keszthely, H-8360 Keszthely, Deak F. u. 16, e-mail: lehoczky@georgikon.hu, kismanyoky.a@georgikon.hu 2 University of Debrecen, Faculty of Agronomy Debrecen, H-4032 Boszormenyi u. 138. Debrecen, e-mail: tamas@gisserver1.date.hu, pburai@gissserver1.date.hu * Corresponding author Summary Since the decision of The Earth Summit in Rio (UN 1992), conservation of biodiversity is a critical environmental protection issue. However the sector specific arable land agro biodiversity was only defined a few years ago in OECD countries. The one-sided usage of herbicide led to the selection of several weed species, and some species need protection. In this study, we analyzed the rate of weed coverage and population in a 22 year long term field experiment. A coenological survey has been performed simultaneously to a maize field production test with the application of increasing doses of NPK and NPK-FYM. Parallel with the conventional mapping, we also used a wide broad handy camera, taking images in NIR-R-G spectra, for mapping. The results of the image analysis significantly improved the reliability of field surveying in case of high weed cover (>25 %) compared to traditional surveying methods. Regarding NPK and NPK-FYM treatments: the results of the statistical analysis (Student’s t test) showed that the values of NDVI, weed cover (%) and the visual coenological survey (%) were significantly higher (SD = 5 %) for NPK-FYM treatments. We expressed the changes of the weed species constitution with the Shannon-index, well known in ecology. We evaluated the development of species number according to the different relations of nutrient supply. The application of increasing fertilizer doses reduced the number of occurring species in both treatments compared to control treatments. The information gained is suitable for the indication of biodiversity of other production sites, and can be a basis for optimising the cropping technology interferences. Keywords: Multispectral imaging, weed control, coenological survey Zusammenfassung Vergleichende Untersuchungen zum Einfluss von Düngungseffekten auf die Unkrautvielfalt bei Langzeit- experimenten mit Methoden der Bildverarbeitung Seit der Entscheidung auf der Konferenz der Vereinten Nationen für Umwelt und Entwicklung in Rio (UN 1992) ist die Erhaltung der Biodiversität ein entscheidender Faktor im Umweltschutz. Allerdings wurde die spezifische Agrobiodiversität erst in den letzten Jahren in OECD Ländern definiert. Die einseitige Nutzung von Herbiziden führte zur Anreicherung einzelner Unkrautarten, andere benötigen Schutz. In dieser Untersuchung analysierten wir das Unkrautvorkommen, den Unkrautdeckungsgrad und die Population einiger Unkräuter in Rahmen eines seit 22 Jahren laufenden Feldversuchs. In dem Versuch wurden die phytozönologischen Werte einer zunehmenden NPK und NPK-FYM Behandlung von Mais ausgewertet. Parallel zur herkömmlichen Kartografie wurde eine multispektrale NIR-R-G Kamera benutzt. Die Ergebnisse der multispektralen Bildverarbeitung verbesserten die Zuverlässigkeit des Feldversuchs
802 LEHOCZKY, TAMÁS, KISMÁNYOKY, BURAI
gegenüber den konventionellen Methoden bei hohen Unkrautdeckungsgraden (>25 %). NPK Behand-
lungen und NPK-FYM Behandlungen: im Bezug auf den errechneten NDVI, der Unkrautdeckungsgrad
(%) und die optische phytozönologische Bewertung sowie die Werte des Student Tests (SD = 5 %)
weichten signifikant in den NPK-FYM Parzellen ab. Wir haben die Änderungen der Unkrautpopulation
mit dem aus der Ökologie bekannten Shannon Index ausgedrückt. Die Herausbildung der Arten wurde in
Abhängigkeit vom Nährstoffangebot ausgewertet. Die zunehmende Nährstoffdosis minderte in beiden
Fällen die Zahl einzelner Unkrautarten gegenüber der Kontrolle. Die so gewonnenen Informationen sind
für die Angabe der Biodiversität anderer Standorte geeignet und könnten die Basis einer Optimierung des
Anbausystems bilden.
Stichwörter: Multispektrale Bildverarbeitung, Unkrautbekämpfung, Phytozönologie
Introduction
Sampling sites have always given rise to heated debates in the course of coenological survey. Accuracy of
detection spatial weed patches is also important since we intend to use weed survey for planning the weed
control programme (CARDINA et al. 1992, REISINGER 2001). Remote sensing and associated spatial
technologies provide good opportunity to enhance weed management and improve protection of the
environment through judicious use of the most effective control methods for a given site (TAMAS 2001,
SHAW 2005).
The study was conducted in a 22 year old long-term field experiment in Keszthely, Hungary. The long-
term fertilization experiment (IOSDV) was set up in 1983 on the research field of the Department of Soil
Management and Land Use at the University of Veszprém Georgikon Faculty of Agriculture (LEHOCZKY
et al. 2004, 2005). The Balazs-Ujvarosi method (weed cover, species composition) which we applied
earlier was completed as follows: in the sample areas (1 m2) with image processing the weed cover value
and the NDVI index was determined, from which the biomass can be concluded, as well as based on the
cenology survey, the Shanon index was determined which characterises the frequency relations of the
species, but gives no information on the concrete species, the species composition of the weed flora.
Materials and methods
The long-term fertilization experiment as a bi-factorial trial was arranged in split plot design with three
replications. Size of plots: 48 m2 (6 m x 8 m). Crop rotation: maize (M) – winter wheat (WW) – winter
barley (WB). Factor A: nutrients: a1 (I) NPK; a2 (II) NPK+35 t/ha FYM (plowed in maize). Factor B:
N kg⋅ha-1 N0-N4, in all treatments: 100 kg P2O5 ha-1 & 100 kg K2O ha-1 (Tab. 1).
Tab. 1: The applied treatments in the experimental plots.
Tab. 1: Die Behandlungsvarianten der Versuchsparzellen.
Treatments M WW WB
Codes N [kg⋅ha-1]
N0 - - -
N1 70 50 40
N2 140 100 (50+50) 80
N3 210 150 (50+50+50) 120 (80+40)
N4 280 200 (100+50+50) 160 (80+40+40)
In 27 May, 2005 the weed survey was carried out in maize according to the Balazs-Ujvarosi method
(REISINGER 2001). The Balazs-Ujvarosi weed estimation method is a visual, cenological one based on
weed estimation, widely used. In Hungary there were 4 National Weed Surveys (1947-1997) which were
carried out by the Balázs-Ujvárosi method.Fertilization and weed biodiversity 803
Weed canopy was estimated as total and by species. During the study the two nutrient treatments (NPK,
NPK+FYM), were compared, the same method was applied in both treatments. The Balazs-Ujvarosi and
image surveys were done at the same time.
Before the weed survey there was no weed control in the experiment. Maize was sown on May 2,
2005.
For vegetation mapping and weed canopy cover detection the newly developed (2000) Tetracam ADC
handy camera was used. This multispectral agricultural camera offers low cost and utilizes a single 1.3
million pixel sensor to deliver accurate red, green and near infrared data sources.
Basic camera features include C-mount optics for extreme flexibility of imaging assignments,
CompactFlash image storage, USB interface, a color LCD display for framing and review. GPS
referenced review of either raw R/G/NIR images or IPVI is also possible in camera by field conditions.
Reflectance variations of vegetation on the image are attributed to the different species of vegetation and
their densities. Normalized Difference Vegetation Index (NDVI) for the 2 m x 2 m plots were expressed
based on parallel traditionally coenological survey and handy camera NDVI data as follows:
NDVI = (NIR Band -Red Band) / (NIR Band + Red Band)
The Normalized Difference Vegetation Index (NDVI) tends to be lower in lower biomass (TUCKER
1979). In this project the UC Davis method was used to determine the canopy cover based on geo-
referenced canopy-cover images. We applied in this experiment the biodiversity index was developed by
SHANNON and WEAVER (1949) and calculated as follows:
s
H = −∑ pi ln pi
i =1
where H is the measure of biodiversity, s equals the number of species and pi equals the ratio of
individuals of species i divided by all individuals N of all species. The coenological survey resulted in
more complex weed patches based on the frequency of the number of present species and the common
spatial occurrence of individual species.
Statistical analysis (Student’s t test, Pearson correlation coefficient, regression analysis) was
performed with SPSS 12 software, DGPS geodesic measurements were carried out with TRIMBLE
survey analyst and ENVI 4.2 software was used for image analysis. The mathematical-statistical analysis
of the data was done by MS Excel and ANOVA-SPSS.
Results
Based on both weed surveying methods it can be stated that organic fertilizer/manure treatment increased
the scale/extent of total weed cover. In case of inorganic fertilizer treatment the increasing doses reduced
the scale/extent of total weed cover. The highest weed cover was measured at the N2 NPK-FYM
treatment. A significant difference in total weed cover was found between the NPK and NPK FYM
treatment at SD (5 %) with t-test.
Species diversity decreased in both treatments compared to control plots. In the NPK FYM treatment
the higher weed cover was caused by less species, which was finally reduced to four species with the
increasing doses. With the application of dose N3 the weed cover was 81.5 % while at dose N4 it reached
91.8 %. The tendency of decreasing number of species and diversity was similar at the NPK treatment
although its extent was higher: at dose N3 87 %, and at dose N4 98 %. The dominant species composition
changed however the number of species decreased more drastically. The increased nutrient supply
contributes to the decreasing of biodiversity which was less pronounced with the application of organic
fertilizer although caused a higher total weed cover (Tab. 2).804 LEHOCZKY, TAMÁS, KISMÁNYOKY, BURAI
Tab. 2: Detected species and their frequency (plot number n = 15 /manure method).
Tab. 2: Observierte Unkrautarten und deren Auftreten (Anzahl der Parzellen n = 15/Düngemethode).
Avg.1 Avg.1
Rank
Species - NPK Canopy Present Species - NPK FYM Canopy Present
[%] [%]
1 Chenopodium album 5.34 15 Abutilon theophrasti 28.76 15
2 Amaranthus chlorostachys 3.60 15 Convolvulus arvensis 7.67 8
3 Convolvulus arvensis 1.63 7 Chenopodium album 3.46 15
4 Lathyrus tuberosus 0.32 1 Chenopodium hybridum 0.81 2
5 Ambrosia artemisiifolia 0.29 6 Amaranthus chlorostachys 0.46 11
6 Abutilon theophrasti 0.19 7 Xantium strumarium 0.34 3
7 Cirsium arvense 0.17 1 Polygonum persicaria 0.05 2
8 Xantium strumarium 0.09 4 Ambrosia artemisiifolia 0.02 1
9 Echinochloa crus-galli 0.05 6 Echinochloa crus-galli 0.01 2
10 Veronica hederifolia 0.04 1 Veronica hederifolia 0.01 1
11 Chenopodium hybridum 0.01 1 Cirsium arvense 0.01 1
12 Polygonum persicaria 0.01 1
Avg.1= Average
Biodiversity concerns spatial variability of species and individuals within ecosystems. Biodiversity is
defined as the variability in space among living organisms and the ecological complexes of which they
are part; this includes diversity within species, between species and of ecosystems. The technology
oriented Balázs-Ujvárosi system (UJVÁROSI 1973, HUNYADI 1988) of weed classification, which is widely
used in Hungary, has to be upgraded in agri-environmental respects.
Several indices were developed for the measurement of biodiversity which can be used individually or
in combination for the calculation of certain features such as species richness (LUDWIG and REYNOLDS
1988, MAGURRAN 1988, KREBS 1989). The application of the Shannon diversity index is less known for
agricultural practice, however besides the assessment of natural biotopes it can also be used for gene
mapping of crops for example: Sorghum bicolor (ALDRICH et al. 1992, ABDI et al. 2002) Triticum
aestivum (NEGASSA 1986).
Shannon Index shows the biodiversity of the weed species in the examined two treatments.
Accordingly the values of the index decreased in inverse ratio to the increasing applied nutriment doses.
Higher number of species resulted more balanced weed cover in the NPK treatment, so the value of H
was 1.41 in the average of treatments, till in the NPK-FYM treatment two species caused very high cover
by less species number, which could be appreciated by lower Shannon-index, H = 0.95. Student’s t test
showed significant differences between treatments (SD 5 %=0.099). With the increasing nutrient levels
the highest N4 doses significantly reduced biodiversity in both treatments compared to control. No
consistent tendency in the level of biodiversity could be observed in case of treatments with lower doses
(Tab. 3).
Tab. 3: Shannon indices in the two treatments with the increasing nutrient supply.
Tab. 3: Shannon Index Werte bei zunehmender Nährstoffversorgung.
Treatments Codes N0 N1 N2 N3 N4
Shannon indices of
NPK treatment 1.52 1.23 1.7 1.19 0.82
Shannon indices of
NPK-FYM treatment 1.2 1.07 0.56 0.99 0.63Fertilization and weed biodiversity 805
TETRACAM ADC was developed for agricultural purposes; it can be used in environmental or plant
protection practice (NAGY et al. 2004). The TETRACAM measurements allowed the calculation of
precise coverage values. The visual coenological survey generally underestimated the weed coverage
values. In case of NPK treatments Pearson correlation coefficient was r = 0.91 between the results of
visually assessed and camera assessed values of coverage, while in case of NPK-FYM treatments it
accounted r = 0.19. The subjective evaluation ensured good accuracy in case of NPK treatments with low
coverage, while in case of NPK-FYM treatments with nearly twice as high coverage it proved to be
inaccurate. Regarding NPK and NPK-FYM treatments: the results of Student’s t test showed that the
values of NDVI, coverage (%) and the visual coenological survey (%) were significantly higher
(SD = 5 %) for NPK-FYM treatments. (Tab. 4).
Tab. 4: The canopy values of handy camera measured and traditionally visualised coenological survey
on A, NPK treated B, NPK-FYM treated plots.
Tab. 4: Bedeckungsgrade der bei A, NPK behandelten und B, NPK-FYM behandelten Parzellen auf
Basis des mit der Kamera gemessenen Bedeckungsgrades und der herkömmlichen phyto-
zönologischen Erfassung der Parzellen.
Treatments Codes N0 N1 N2 N3 N4
A, TETRACAM-NDVI 0.53 0.56 0.58 0.41 0.34
A, TETRACAM-Canopy (%) 18.26 20.06 26.5 11.17 6.55
A, Visual coenological survey (%) 9.00 14.82 20.9 8.18 5.77
B, TETRACAM-NDVI 0.55 0.68 0.61 0.6 0.53
B, TETRACAM-Canopy (%) 40.6 62.8 47.76 45.9 37.46
B, Visual coenological survey (%) 55.05 43.26 45.56 36.06 28.06
The reason for inaccuracy of the visual coenological survey was partly the optical delusion caused by the
row directional assessment. The overall accuracy of supervised classification of the digital images was
0.94 ± 0.054. This is calculated on each picture by summing the number of pixels classified correctly and
dividing by the total number of pixels. The applied area as spectral teaching area was 5 % of the total
image. The calculated NDVI and the weed canopy values showed very close correlation for either the
NPK (r = 0.96), or the NPK-FYM treatment (r = 0.86) (Fig. 1).
Fig. 1: Correlation values and linear trends between NDVI and canopy (in %).
Abb. 1: Korrelationswerte und lineare Regression zwischen NDVI und dem Bedeckungsgrad (in %).806 LEHOCZKY, TAMÁS, KISMÁNYOKY, BURAI
The NDVI index and the canopy cover mapping can be useful for detecting distinct weed segmentation
and their spatial patches (Fig. 2).
Fig. 2: Near Infrared picture and results of applied Soebel filter for weed segmentation on NPK N3
treated plot.
Abb. 2: Nahes-Infrarot-Bild und Resultat des angewendeten Soebel Filters zur Segmentierung der Un-
kräuter auf NPK N3 behandelten Parzellen.
In broad based channels it is impossible to classify spectrally attributes of crops and weeds because
reflectance of the green biomass is very similar, but spatially maize and weed patches can be
distinguished if the canopy percentage is lower than 15 %. From the applied different filtering methods,
the Soebel filter with 3 x 3 kernel size gave the best results.
Acknowledgement
Financial support of this work was provided by the Hungarian Research Fund (OTKA No T046845,
OTKA No T047366). Acknowledgement for the OTKA No K60314.
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