Minimum Dataset
Deliverable 4.2
December 2012
1
Table of Contents
Executive summary.....................................................................................................................3
Introduction .................................................................................................................................4
Variable-rate irrigation................................................................................................................4
Irrigation scheduling .................................................................................................. 5
Results on water consumption ................................................................................. 14
Conclusions on precision irrigation ......................................................................... 18
Variable-rate fertilization ..........................................................................................................18
Fertilizer application ................................................................................................ 18
Crop response to variable-rate fertilizer application ................................................ 19
Comparison of variable-rate fertilizer application to farmer practice ..................... 20
Correlation of canopy reflectance to soil properties and cotton yield ..................... 21
Requirements for adoption of the technology.......................................................... 21
Summary and conclusions ....................................................................................... 23
Site-specific pest management ..................................................................................................36
Herbicide application ............................................................................................... 36
Insecticide application ............................................................................................. 36
Pesticide Use Efficiency .......................................................................................... 36
Digital weed mapping .............................................................................................. 37
Requirements for site-specific pest management .................................................... 38
Conclusions – Management decisions ..................................................................... 40
Parameters related to the development of regional GIS database.............................................41
Methodology ............................................................................................................ 41
2
Results ...................................................................................................................... 42
References .................................................................................................................................44
Minimum Dataset
Executive summary
The ground sensor technologies used in this project allowed the remote estimation of
spatially-variable fertilizer and irrigation water within the growing season. When inputs
estimated this way were applied to management zones that were delineated by differences
in soil organic matter content, there was a significant reduction of water, chemical and
energy inputs and the crop responded by an average of 10% yield gains in 2011.
Considering the excessive and unguided applications by the farmers in the area, a
substantial reduction of fertilizer and water inputs can be achieved by the adoption of
multispectral sensors, soil moisture sensors and evapo-transpiration devices for uniform
application of entire fields before somebody considers spatially-variable inputs. Most
farmers in the area already possess the equipment needed to uniformly apply fertilizer and
irrigation water at recommended rates without additional costs.
For more efficient management of inputs within single fields, delineation of management
zones and a spatially-variable application system is needed. However, variable rates of
fertigation in different management zones would require a differential drip irrigation system
which is associated with additional costs and farmer involvement. A decision support tool
developed by the HydroSense project was the production of high-resolution maps of soil
organic matter distribution across the Pinios watershed using the relationship between bare
soil satellite NIR reflectance and soil nitrogen content. These maps can be used to delineate
simple management zones within fields and, thus, reduce farmer costs associated with
setting-up differential fertigation systems. A sound alternative to variable-rate fertilizer
application by management zone is the employment of real-time and variable-rate
technology that is expected to become commercially available in the next few years.
Whether the ground sensor systems are used for uniform or spatially-variable fertilizer
application within fields, they are associated with purchasing costs, farmer training and
involvement. A feasibility study is required to identify possibilities and reach further
conclusions for the adoption of this technology by individual farmers or networks or for
provision of services by agricultural unions and private companies.
Greater incentives for the adoption of the irrigation and fertilizer technologies can be
provided through subsidies for the protection of environmentally sensitive areas or through
the adoption of water pricing policies by local government.
3
Introduction
This deliverable concerns the assessment of site-specific management in the demonstration
sites during the summer growing seasons of 2010 and 2011. The site-specific management
practices were applied by management zone in the cases of irrigation and N fertilization and
by real-time variable-rate in the case of weeds. The report evaluates the degree of success
of these new approaches at the field scale in terms of crop response, environmental
performance and potential of adoption by producers.
This deliverable also examines the potential of modeling key soil and plant properties that
can be used to predict productivity patterns or delineate management zones at the
watershed scale. For this purpose, soil or crop properties should correlate to individual
wavebands or vegetation indices of satellite imagery so that classification and mapping of
the entire watershed can be achieved for the purpose of regional-scale planning.
Variable-rate irrigation
The developed variable-rate irrigation system was able to provide irrigation water to each
management zone independently. Figure 1 is a schematic layout of the components which
make up the irrigation system used. The main supply of irrigation water (after pumping from
irrigation canals, or from irrigation network) connected with a filtration system and a
fertilizer injection device. A number of different drip irrigation lines ran along the crop rows
to deliver water to the different management zones. A faucet and a hydrometer connect the
drip irrigation pipes with the main system, thereby allowing the selective irrigation of the
desired management zone and the measurement of total irrigation water applied.
4
Figure 1. Schematic presentation of the differential drip irrigation system used in Gyrtoni. Each of the
three management zones is provided with an independent drip irrigation network. The
drip irrigation lines run between the cotton rows.
Irrigation scheduling
Each management zone of the pilot areas was regularly monitored for the entire irrigation
period in terms of water needs with the different systems of sensors. Every type of sensor
gave different information:



Smart Crop infrared system: Timing of irrigation, on-line monitoring of irrigation
efficiency
Watermark soil moisture sensors: evaluation of the effectiveness of rain and
irrigation water, timing of irrigation
Evapotranspiration device: volume of irrigation water to apply
Smart Crop data for each management zone were continuously downloaded to the website.
Soil moisture and evapotranspiration data were recorded manually every 2 days.
The experience gained in the 2010 season was used for more timely and efficient use of the
systems in 2011. In Gyrtoni and Omorfochori pilots, the timing of irrigation was determined
by the combined utilization of the Smart Crop system and Watermark sensors. When canopy
temperature began to decrease approaching ambient and watermark sensor readings were
over 80-100kPa, then irrigation started. In the Gyrtoni pilot, the timing of irrigation was
based solely on watermark readings because the Smart Crop system was not operational.
Pilot plots were irrigated the same way to the controls, that is, uniformly during the first 6
irrigation events. In this way we avoided any water stress in the first growth stages of
cotton that could have a yield-limiting effect. Variable rate irrigations started in early July
2011 when the crop started to demand greatest amounts of water. It was day 72 after
planting for Gyrtoni, 74 for Omorfochori and 75 for Gentiki.
Volume of irrigation was based mainly on the ET gage data. According to the growth stage of
the crop, water demand was calculated and appropriate amount of irrigation water was
applied in each management zone. Differences in the volume of irrigation water in the
management zone of the same field reflect different soil water content as indicated by the
soil moisture sensors (Figure 2).
Differences in soil properties between management zones were met with the variable
application of irrigation water. In Gyrtoni, higher concentration of clay and soil organic
matter in zone A resulted in higher soil water holding capacity and less extreme variations in
soil water content compared to zone B (Figure 2).
5
Figure 2. Watermark soil moisture readings by management zone for the 2011 irrigation
period at Gyrtoni. Red lines indicate irrigations events.
Evapotranspiration measurements were performed with ET gage every 2 days after cotton
planting. Cumulative evapotranspiration for each of the pilot plots is shown in Figures 3, 5
and 7. Total evapotransiration for the Gyrtoni pilot plot was 729 mm.
Figure 3. Cummulative evapotranspiration estimated by ETgage for the 2011 irrigation
period at Gyrtoni
6
Variable-rate application of irrigation water resulted in different water volumes in the
management zones reative to the control. Total irrigation water was also different among
management zones (Table 1).
Table 1. Volume of irrigation water applied (mm) at Gyrtoni in 2011 irrigation period
Zone A
Zone B
Control
27/4
20
20
20
15/5
30
30
30
30/5
35
35
35
14/6
45
45
45
23/6
38,5
38,5
38,5
4/7
38,5
38,5
38,5
Variable rate irrigation
11/7
55,6
57,6
66
19/7
57,8
31,4
66
23/7
28,9
16,7
38,5
27/7
30,6
22,4
38,5
2/8
-
-
38,5
8/8
61,7
59,6
38,5
17/8
49,9
33,7
44
20/8
31,09
43,2
44
30/8
0,44
12,0
38,5
Total
523
483
619
In the Omorfochori pilot, management zones responded to irrigation events in different
ways and in some cases the volume of irrigation water differed between management
zones. For example, zone A received a low volume of irrigation at 90 days after planting
resulting in the continued decrease of soil moisture (increased sensor values). The opposite
trend occurred at the same date in Zone B and C indicating more effective irrigation (Fig. 4).
Ineffective irrigation was also evident in Zone B 95 days after planting in contrast to Zone A
and C. Total evapotranspiration of the Omorfochori field was measured to be 737mm and
was almost the same to that of the Gyrtoni field (Fig. 5).
7
Figure 4. Watermark soil moisture readings by management zone for the 2011 irrigation
period in Omorfochori. Red lines indicate irrigation events.
Figure 5. Cumulative evapotranspiration measured by ETgage for the 2011 irrigation period
in Omorfochori
8
Table 2. Volume of irrigation water applied (mm) in Omorfochori during the 2011 season
Zone A
Zone B
Zone C
Control
3/5
30
30
30
30
10/5
25
25
25
25
25/5
30
30
30
30
7/6
35
35
35
35
18/6
40
40
40
40
30/6
40
40
40
40
Variable rate irrigation
5/7
45
45
45
57
12/7
50
50
50
57
18/7
33
33,4
33,4
38
21/7
25,8
38,9
26,2
38
27/7
9,7
51,2
34
38
3/8
40,4
16
46,2
47
10/8
13,6
21,4
15,5
38
17
17
17
17
15/8
26,6
42,1
30,5
38
22/8
35,3
50,1
39,3
47
30/8
21,8
29,4
24,4
38
Total
518,2
594,5
561,5
653
10/8 rain
9
Figure 6. Watermark soil moisture readings by management zone for the 2011 irrigation
period in Gendiki. Red lines indicate irrigation events.
Figure 7. Cummulative evapotranspiration estimated by ETgage for the 2011 irrigation
period in Gentiki
10
Overall results indicated that the soil water sensors were able to monitor the soil water
status and that measurements recorded by the systems reflected general trends of soil
water fluctuations during the growing season.
In Gentiki, the management zone with the higher concentration of soil organic matter and
greater water holding capacity received the smallest amount of irrigation water. In contrast,
zone C received 21% more irrigation water than zone A because of its low organic matter
and water holding capacity. This confirms the basic reasoning that soil organic matter and
clay content affect the volume of irrigation water applied in a spatially variable field.
Table 3. Volume of irrigation water applied (mm) in Gendiki during 2011 season
Zone A
Zone B
Zone C
Control
10/5
25
25
25
25
26/5
20
20
15
15
13/6
50
50
50
50
26/6
55
55
55
55
6/7
60
60
60
60
Variable rate irrigation
12/7
57
57
57
57
18/7
65,4
44,0
73,7
57
22/7
22,9
20,9
33,2
38
26/7
18,4
22,6
18,0
38
1/8
5,9
42,2
45,8
57
10/8
32,9
33,0
54,0
57
15/8
9,0
24,8
42,2
38,0
20/8
15,9
15,2
26,1
38,0
25/8
23,5
21,3
32,3
38,0
1/9
17,3
15,7
25,2
19,0
ΣΥΝΟΛΟ
478
506
612
642
11
The daily crop evapotranspiration (ET) for cotton was estimated in each pilot area. The
cumulative ET between the 3 pilots differed by about 2% - 7%. Farmers irrigate their plots
with 85% up to 93% of the estimated evapotranspiration. The pilots were irrigated with 69%
to 88% of the estimated evapotransiration which equated to 4% - 25% less than the controls
(Table 4).
Table 4. Comparison of 2011 irrigation data between pilot areas
Field
Gyrtoni
Omorfochori
ET(mm)
Management Irrigation
Zones
water(mm)
Percentage of
irrigation water to
control (%)
729
Control
619.5
84.9
100
A
523.11
71.7
84.4
B
594.5
81.5
95.6
652
88.4
100
A
518.2
70.3
79.4
B
594.5
80.6
91.1
C
561.5
76.1
86.1
642
92.7
100
A
478.2
69.1
74.4
B
506.7
73.2
78.9
C
612.5
88.5
95.4
737
Control
Gentiki
Percentage of
irrigation water to ET
(%)
692
Control
12
Table 5. Pearson correlations for irrigation data of the Gyrtoni pilot area
ET_cum
Irrigation
Watermark
Canopy_temp
Zone A
Irrigation
-,334
Watermark
,347
-,252
Canopy_te
-,426
,282
-,015
δΘ
-,010
,006
-,239
-,392
Zone B
Irrigation
-,349
Watermark
,060
-,220
Canopy te
-,259
,103
,057
δΘ
-,063
-,002
-,201
-,612
Significant correlations between irrigation water, evapotransiration and canopy temperature
were detected in some management zones of Gyrtoni and Omorfochori pilots. More
specifically, canopy temperature of cotton plants was strongly correlated to
evapotranspiration in Omorfochori (Table 6). Effects of irrigation on canopy temperature
and soil water were observed with an increase in soil water within the root zone as shown
by irrigation-canopy temperature correlations. This does not apply for Gyrtoni (Table 16) but
there are several local and seasonal environmental factors (sunshine, air temperature, wind
and humidity) which affect canopy temperature independent of transpiration that cause a
loss of soil water (Padhi et al., 2012).
Table 6. Pearson correlations for irrigation data of the Omorfochori pilot area
ET_cum
Irrigation
Watermark
Zone A
Irrigation
Watermark
Canopy_temp
-,050
,250
,158
-,703
,445
13
-,099
Canopy_temp
δΘ
,492
-,276
,207
-,887
Zone B
Irrigation
-,020
Watermark
-,085
,138
Canopy_temp
-,773
,455
-,109
,592
-,296
,107
δΘ
-,887
Zone C
Irrigation
Watermark
Canopy_temp
δΘ
-,044
,344
-,143
-,679
,386
,025
,384
-,223
-,431
-,855
Although the monitoring of canopy temperature was useful for the timing of irrigation at
Gyrtoni and Omorfochori, irrigation scheduling could only be performed by the monitoring
of soil moisture and evapotranspiration in Gentiki.
There are several levels at which canopy temperature data can be instructive. In real time or
short (daily or weekly) timescales, temperature can be monitored to indicate irrigation
system performance, rainfall utilization and the current stress status of the plant. The
installation of a number of IR sensors strategically placed within a field or within
management zones will allow the producer to see water deficits spatially within an irrigation
system. When in-season rainfall provides a significant portion of the crop’s water
requirement, monitoring of canopy temperature can result in significant reduction of
irrigation water because it provides plant-based information on the water status of the crop.
Since canopy temperature is collected on short time intervals (15 min in this study), it can
provide a level of resolution that is unequaled by virtually all other measures of the plant.
However, there are several issues affecting the performance of the Smart Crop system
during field operation that seem to be important obstacles for its adoption by farmers
without a relevant familiarity with the technology. Nonetheless, farmers are being
challenged to practice conservation methods and use water resources more efficiently while
meeting plant water requirements and maintaining high yields.
Results on water consumption
The results regarding water use in the 3 experimental fields include the water consumption
for the entire irrigation period, the seed yield and the water use efficiency.
14
Water consumption refers to the sum of irrigation water applied and rainfall received during
the growing season expressed in mm water. The yield expressed in kg of seed cotton /ha and
irrigation water use efficiency was calculated from the water consumption and yield and
expressed as kg of seed cotton /m3 irrigation water.
Results from the Gentiki demonstration site are presented in Figure 8. With precision
irrigation zone A received 34,23% (478,2mm H2O), zone B 26,7% (506,7mm H2O) and zone C
4,8% (612,5mm H2O) less irrigation water than was applied in the control (642 mm H2O).
Cotton yield in zone A increased by 17,5%, in zone B increased by 11,2% and in zone C
decreased by 1,4% in comparison to the corresponding zones of the control. Therefore,
water use efficiency increased significantly in two management zones. Irrigation water use
efficiency in Zone A (0.50 kg /m3 ) was 34,38% higher than the control (0.37kg /m3), in zone
B (0.59kg /m3) 20% higher than the control (0.49kg /m3) and in zone C (0.45kg /m3) 11,9%
less than the control (0.51kg /m3).
WATER CONSUMPTION (mm H2O)
COTTON YIELD (kg/ha)
IRRIGATION WATER USE EFFICIENCY (kg/m3)
15
Figure 8. Results of water consumption, cotton yields and water use efficiency in the Gentiki
demonstration site
Results from Omorfochori demonstration site are presented in figure 9. With precision
irrigation zone A received 26% (518,2mm H2O), zone B 9.8% (594,5mm H2O) and zone C
16,3% (561,5mm H2O) less irrigation water than that applied in the control. Cotton yield in
zone A increased by 8,7%, in zone B by 14,5% and in zone C by 31% in comparison to the
corresponding zones of the control. Irrigation water use efficiency in Zone A (0.66 kg /m3)
was 27,3% higher than the control (0.48kg /m3), in zone B (0.61kg /m3) 19.7% higher than
the control (0.49kg /m3) and in zone C (0.71kg /m3) 35,2%less than the control (0.46kg
/m3).
WATER CONSUMPTION (mm H2O)
COTTON YIELD (kg/ha)
IRRIGATION WATER USE EFFICIENCY (kg/m3)
Figure 9. Results of water consumption, yields and irrigation water use efficiency in
Omorfochori experimental field
16
Results from Gyrtoni demonstration site are presented in Figure 10. With precision irrigation
zone A received 18.4% (523,11mm H2O) and zone B 28,1% (594,5mm H2O) less irrigation
water than the control. Cotton yield in zone A decreased by 10,9% (4154 kg/ha) and in zone
B by 18% in comparison to the control (4664 kg/ha). Water use efficiency obtained in Zone A
(0.7 kg /m3) was 4,9% higher than the control (0.67kg /m3) and in zone B (0.79kg /m3) 5%
higher than the control (0.49kg /m3).
WATER CONSUMPTION (mm H2O)
COTTON YIELD (kg/ha)
IRRIGATION WATER USE EFFICIENCY (kg/m3)
Figure 10. Results of water consumption, cotton yield and water use efficiency in Gyrtoni
experimental site.
17
Conclusions on precision irrigation
The results indicated the great potential of precision irrigation to reduce irrigation water
without adversely affecting cotton yields in the region of the Pinios River Basin. Since yield
data are directly affected by climatic conditions, more years of experimentation are required
to precisely quantify the extent of water savings with the adoption of the precision irrigation
techniques used.
Current attitudes of farmers towards irrigation, fertilization and crop protection practices
are based mostly on their empirical assumptions concerning the needs of the crop, with
minimal consideration given to the interlinked environmental impact, energy consumption
and potential cost savings.
Precision agriculture can contribute to more efficient irrigation of cotton in the region with
environmental and economical benefits. The adoption of these technologies by farmers
remains a critical issue because of the need to link new management practices with clear
economic benefits. Farmers may not be easily convinced of the capacity of the novel
technologies introduced to them, which seems to challenge their acquired empirical
knowledge and experience gained over the years on fundamental crop management as
many of them are totally unaware of new irrigation technologies.
The application of precision irrigation by the HydroSense project indicated that soil moisture
sensors and evapo-transpiration devices can be readily adopted by farmers regardless of the
creation of different management zones within the fields. The reasons are their low
purchasing costs, simplicity of use and data interpretation. In this project, evapotranspiration was used to estimate volume of irrigation water and soil moisture sensors
were used to determine timing of irrigation. The production of soil organic matter, soil
texture or cotton yield maps will allow the strategic or guided placement of soil moisture
sensors within a field. It is estimated that the adoption of the above technologies could cost
under 40 euros/ha. Training of farmers on the use of these instruments could be achieved by
the creation of a network between farmers and researchers in the region and expanded to
include other innovative technologies in the future for more efficient water use in the Pinios
River Basin.
The ability of the infrared sensors to directly detect crop water stress could provide growers
or consultants more effective methods to manage water stress by management zone within
fields. However, the cost of these tools coupled with needed expertise for their use make
their adoption by the farmers a difficult task at present.
Variable-rate fertilization
Fertilizer application
The applied method for spatially-variable fertilizer application used a N application model
that directly relates normalized sensor measurements to a generalized plant growth function
(Holland and Schepers 2010). The sensor measurements were in the form of a Chlorophyll
18
Index (CI=( NIR / Red edge)-1) that was computed from individual waveband reflectance
values of the Crop Circle sensors. This vegetation index has been shown to accurately
estimate (R2=0.95) the canopy chlorophyll content (g m-2) under contrasting conditions of
canopy architecture and leaf structure represented by species of soybean and maize. Thus,
CI can be applied to estimate canopy chlorophyll in other crops or under mixed pixel
scenarios (Gitelson et al. 2005). The sensor normalization technique used by the N
application model is referred as the sufficiency index (SI) which is the ratio of the sensed
canopy CI to a reference CI. A unique reference CI was used for each growth stage of the
crop in each pilot area by the identification of non-N limiting plants from the extracted CI
maps (Holland and Schepers 2011). The N application model has the following mathematical
form:
where Nopt is the maximum N rate or the crop N assimilating capacity, Nprefert is the sum
of fertilizer N applied prior to crop sensing, Nom is the N credit for the field’s average
organic matter content, SI is the sufficiency index, ΔSI is the sufficiency index difference
parameter, m is the back-off rate variable (0<m<100) and SIt is the back-off cut-on point.
Segmented fertilizer application within the growing season occurred in 2 to 4 increments of
20 to 40 kg N/ha in the pilot areas via the drip irrigation system (Table 7). The amount of
fertilizer to be applied in each management zone was calculated by the N application model
that converts canopy CI to N requirement. In practice, the management zones of some pilot
areas received the same amount of N in the initial fertilizer applications due to delays in the
installation of the irrigation systems (2010) or due to farmer interference (2011). In these
cases, only the final application determined the difference in the amount of fertilizer
between management zones in the pilot areas (Table 7).
Crop response to variable-rate fertilizer application
The response of canopy chlorophyll index (CI) was positive to within-season fertilizer
applications and indicated that CI was a sensitive indicator of crop N uptake (Fig. 11 and Fig.
12). The magnitude of the response varied with pilot area, management zone and growth
stage of the crop. These interacting factors explain the lack of overall correlation between
canopy CI and rate of N application. For example, the plants of the Gentiki pilot area had a
greater response to fertilizer inputs in both growing seasons in comparison to other pilot
areas. The maximum response occurred with a 20 kg N/ha fertilizer application at 63 DAP of
2011 (Fig. 12) with a 0.65 unit increase of CI corresponding to 150% increase relative to the
CI of the canopy prior to fertilizer application. A smaller increase in CI of 0.20 units in the
previous fertilizer application at 49 DAP (Fig. 12) was attributed to the lower N demand of
younger plants. The effect of late growth stage appears to be the reason for the little CI
response to N inputs at 122 DAP in the Gyrtoni pilot area in 2010 (Fig. 11). The plants in the
Omorfochori pilot area in 2011 responded equally well to N applications at 64 and 73 DAP
(Fig. 12) with an average CI increase by 0.2 units or by 20 to 60% increase relative to the CI
of the canopy prior to fertilizer application.
19
A histogram comparison of CI between pilot areas and sampling times is shown in Figures 13
and 14. Histograms reveal features of the distribution such as skewness, range and double
humps of plant vigor within fields that are not evident in conventional tables of descriptive
statistics. During early growth (up to 73 DAP) there was great variability of plant sizes as
indicated by the wide range of CI and exposure of sensors to bare soil as indicated by the
low CI values in 2010 (Fig. 13). The uniformity of plant growth within fields increased
progressively from 90 DAP to 110 DAP as indicated by narrower CI ranges. The highest CI
values indicated greater plant vigor in the Gentiki pilot area at 110 DAP and lowest CI values
were obtained in the Eleftherio pilot area in 2010 (Fig. 13). Similar temporal patterns were
evident in the 2011 pilot areas although comparative field measurements were available up
to 89 DAP (Fig. 14). During early growth, the histograms of Omorfohori and Gyrtoni had the
appearance of a double hump which is typical when two management zones are expressed
in terms of plant vigor. The disappearance of the double humps and the narrow range of CI
later in the season (Fig. 14) indicated that our differential fertilizer applications
accomplished uniformity of plant vigor within fields. Compared to the other two pilot areas,
Gyrtoni had higher CI values and, thus, greater chlorophyll content from early growth till 89
DAP. The two Gyrtoni fields in 2010 and 2011 at 90 DAP had similar CI histograms both in
terms of range and magnitude.
Differences in CI between management zones were persistent within the growing season in
Gentiki, Gyrtoni and Omorfochori (Fig. 12) despite the application of variable-rate fertilizer
inputs to optimize growth (Table 7). The stability of the differences in CI indicated limitations
in N uptake imposed by differences in soil fertility between management zones. CI
differences between management zones were small in the Eleftherio and Gyrtoni pilots in
2010 (Fig. 11). Within the more effective 2011 season, average maximum CI differences
between management zones were between 10 and 30% that translated into 9 to 13%
differences in applied N fertilizer.
Comparison of variable-rate fertilizer application to farmer practice
A comparison of the timing of fertilizer N applications in the pilot and control areas relative
to crop N demand is shown graphically in Fig. 15 and 16. In-season fertilizer applications
occurred in advance of crop N needs in Gentiki and Omorfohori in 2011 (Fig. 16). The poor
synchronization of soil available N with crop demand indicated increased risk of N losses to
the environment. Due to lack of significant summer rainfall, these losses would be caused by
greater leaching and denitrification in the control areas due to excessive irrigation of the
control areas by the farmer. The total amount of fertilizer applied by the farmers exceeded
crop N assimilation capacity in both fields (Fig. 16) without considering the additional sitespecific N sources available to the crop. These would be of the order of 100 kg N/ha (Table
8) which means that a total in excess of 110 kg N/ha would be lost to the environment in the
control areas. In contrast, the N requirement estimated by the sensor-N model system
resulted in final fertilizer N rates below the crop N requirement in the pilot areas (Fig. 16). By
adding the available site-specific N sources to the applied N (Table 8), an excess of 50-70 kg
N/ha would occur in the pilot areas at the end of the growing season. But this is a safe
margin considering that some fertilizer N losses would incur during the growing season. The
greater yield of the pilot areas justifies our approach. The fertilization procedure in the third
20
pilot area (Gyrtoni 2011) was not as successful despite the appropriate timing of N inputs.
The total amount of available N (fertilizer + site-specific N = 230 kg N/ha) was well below the
exceptionally high crop N demand (300 kg N/ha) in management zones A and B. This
underestimation of crop assimilation capacity, as an integral component of the N application
model, resulted in under-fertilization of the pilot management zones. The greater amount of
fertilizer N applied by the farmer resulted in greater cotton production in the control areas.
The 2010 growing season was characterized by large amounts of early-season soil residual N
in the Gentiki and Gyrtoni sites (Table 8) that was the reason to avoid preplant fertilizer
application and to reduce in-season fertilizer N in the pilot areas by the N application model
(Fig. 15). However, the slightly lower yield attained in the Gentiki and Eleftherio pilot areas,
compared to their respective controls, was perhaps caused by delayed fertilizer applications
at a time when most soil N was taken up by the crop (Fig. 6). The estimates of crop N uptake
were probably underestimated in the Gyrtoni site in 2010 (Fig. 13) due to a severe pest
damage that resulted in reduced cotton yield. For this reason, our N strategies are not
evaluated for this site.
Differences in the amount of fertilizer applied were much greater between the pilot and the
control areas than between management zones within the pilot areas. On average, 57% less
fertilizer was applied in the pilot areas with only an 8% yield loss in 2010 and 34% less
fertilizer with an actual yield gain in 2011 (Tables 9 and 10). These values translate to a great
increase of NUE in the pilot areas, 75% greater NUE in 2010 (Gyrtoni site omitted) and 65%
greater NUE in 2011, which mean respective savings in N losses compared to the farmer
practice under long-term steady-state conditions. The large differences in fertilizer rates and
environmental performance indicators between the control and pilot areas indicate that the
large fertilizer doses by the farmers were excessive despite their attempt to synchronize N
inputs with crop needs by multiple within-season applications.
Correlation of canopy reflectance to soil properties and cotton yield
Canopy CI of management zones, before the application of in-season N fertilizer and
differential irrigation at 70 DAP, was correlated to cotton yield, applied fertilizer, irrigation
water and NUE in different years. But one consistent positive correlation across pilot zones
and years was between CI and WUE indicating that management zones of high canopy CI
values had average to high cotton yields and low needs for irrigation water. This relationship
would suggest that vegetation indices such as CI or NDVI of ground sensors or satellite
imagery could serve as input variables in the GIS model to predict areas of high productivity
and low irrigation needs and provide useful scenarios for water pricing and recovery of
irrigation costs at watershed scale.
Requirements for adoption of the technology
However, the main purpose of monitoring canopy CI was to apply variable-rate N within
fields and the sensor technology is designed for this purpose. The large and excessive
fertilizer doses applied by the farmers indicate that the use of our N application model
would reduce fertilizer inputs, optimize yields and protect the environment. As a first step,
the use of our sensor data in an N application model for the estimation of a uniform
application in the entire field would bring a spectacular improvement in farmer N
21
management practices before somebody considers monitoring N inputs by management
zone. This can be achieved by a single pass of the sensor-carrying vehicle within the growing
season at 70 to 80 DAP after an initial pre-plant application of about 20 to 40 kg N/ha. The
exact amount of preplant N application can be determined by the residual nitrate content in
the soil. The results of the 2011 growing season indicate that a reduction of fertilizer N by
30% can be achieved in this way. This figure was calculated by comparing the maximum
fertilizer rate per management zone of each pilot area to that applied by the farmer. The
sensor-N application model has advantages over the traditional N mass balance approach in
that it accounts and corrects for variable weather conditions and soil organic matter
mineralization during the growing season.
An alternative to the sensor equipment is the use of satellite data in the N application
model. Satellite imagery has the advantage of delivering spectral information rapidly over
relatively large areas of the landscape. The new satellite imagery of World View 2 has the
appropriate resolution and wavebands (including red edge) to compute the spatial variability
of the chlorophyll index within single fields. However, the information provided by airborne
platforms may not be available in time to implement critical management decisions. This is
because the availability of airborne sensor data is constrained by weather conditions, revisit
frequency and elaborate data processing. The ground sensing system used in this project has
no such limitations.
For spatially-variable fertilizer applications, experienced farmers would be able to visually
identify distinct management zones within their fields without the need to employ remote
sensing technologies and ground-truthing procedures. However, different fertilizer rates in
different management zones would require a differential irrigation system considering that
it is a common practice in the area to apply liquid fertilizer through drip irrigation. A
differential irrigation system is associated with additional costs and involvement which the
farmers may not have. A sound alternative to variable-rate application by management zone
is the employment of real-time and variable-rate technology that is expected to become
commercially available in the next few years. Employment of real-time technology does not
require the delineation of fields into management zones and the set-up of differential
irrigation systems while it is designed to maximize yields and NUE by spatially optimizing N
inputs.
Whether the sensor system is used for uniform or spatially-variable N application within
fields, it is associated with purchasing costs, farmer training and involvement. The current
price of N fertilizers will not justify the use of such equipment unless producers manage
larger fields of the order of 100 ha where fertilizer savings can be significant. Most farmers
in the area will manage multiple fields of a total area of 50 ha or less. A feasibility study is
required to identify possibilities and reach further conclusions for the adoption of this
technology by individual farmers or for provision of services by agricultural unions and
private companies. Greater incentives for the adoption of the technology can be provided
through subsidies for the protection of environmentally sensitive areas. This is a realistic
scenario because such subsidies have been given in the past for the protection of
groundwater quality by limiting the use of N fertilizers by farmers in the Thessaly Plain.
22
Summary and conclusions
The sensor technology used in combination with the N application model allowed the
remote estimation of spatially-variable crop N requirement within the growing season.
When fertilizer N estimated this way was applied to management zones that were
delineated by differences in soil organic matter content, the crop responded to each
segmented application and an average reduction of N inputs by 35% with 10% yield gains
was achieved in 2011. The remote estimation of crop N requirement is a superior tool for
spatially-variable and site-specific N management when compared to conventional methods
(mass balance approaches, intensive soil and plant sampling). The lack of significant
correlations between canopy chlorophyll index and measurable soil properties indicated the
magnitude of interacting factors in determining crop N uptake and yield potential and the
need of using this technology on a field-by-field basis. A consistent relationship between CI
and WUE indicated that CI could possibly serve as an input variable in the GIS model to
predict areas of low irrigation needs and, thereby, provide useful scenarios for water pricing
and recovery of irrigation costs.
Considering the excessive and unguided fertilizer applications by the farmers in the area, a
significant reduction of fertilizer N inputs can be achieved by the adoption of this technology
for uniform application of entire fields before somebody considers spatially-variable N
inputs. Remotely-sensed data can be obtained by ground sensors used in this project or by
appropriate satellite imagery and used to estimate N requirement by the N application
model. However, additional information will be needed in order to adjust the N application
model to individual fields (site-specific N sources, expected yield, planting date and timing of
application). Most farmers in the area can provide the required data and possess the
equipment needed to uniformly apply fertilizer at recommended rates with no additional
costs.
For more efficient N management within single fields, a spatially-variable N application
system is needed. However, different fertilizer rates in different management zones would
require a differential irrigation system which is associated with additional costs and
involvement which the farmers may not have. A sound alternative to variable-rate
application by management zone is the employment of real-time and variable-rate
technology that is expected to become commercially available in the next few years.
Whether the sensor system is used for uniform or spatially-variable N application within
fields, it is associated with purchasing costs, farmer training and involvement. A feasibility
study is required to identify possibilities and reach further conclusions for the adoption of
this technology by individual farmers or for provision of services by agricultural unions and
private companies. Greater incentives for the adoption of the technology can be provided
through subsidies for the protection of environmentally sensitive areas.
23
Table 7. Segmented fertilizer applications by pilot area and management zone.
Pilot area
Gentiki
Fertilizer applied (kg N/ha)
Zone A
Zone B
Zone C
40
40
40
23
23
23
20
20
20
43
32
38
126
115
121
-9
-4
Year
2011
Application
1
2
3
4
Sum
% zone diff.
DAP
-21
49
63
79
Omorfohori
2011
1
2
3
4
5
Sum
% zone diff.
-14
64
73
73
90
40
29
17
17
32
135
40
29
17
17
15
118
-13
Gyrtoni2
2011
1
2
3
4
Sum
% zone diff.
-15
86
95
111
50
25
30
13
118
50
15
25
13
103
-13
Gentiki
2010
1
2
3
Sum
% zone diff.
75
96
103
30
23
22
75
30
23
22
75
0
Eleftherio
2010
1
2
Sum
% zone diff.
50
99
50
22
72
35
10
45
-38
Gyrtoni1
2010
1
Sum
% zone diff.
122
35
35
25
25
-29
24
40
29
17
17
25
128
-5
30
23
0
53
-29
35
35
0
25
Table 8. Estimation of site-specific N sources available to the crop, other than fertilizer, in each demonstration site.
Gentiki
2010
Eleftherio
Gyrtoni1
Gentiki
2011
Omorfohori
Gyrtoni2
Early-season soil nitrate N
Early-season available N†
Soil organic N
Legume N‡
Irrigation N
Manure N‡
170
111
40
0
20
0
77
50
40
0
20
0
223
145
40
0
20
0
40
26
40
0
20
0
62
40
40
0
20
0
93
60
40
0
20
0
Total available N sources
171
110
205
86
100
120
† assuming 35% N losses during the growing season
‡ sites had no recent history of legume rotations or manure applications
26
Table 9. Comparison of fertilizer application and nitrogen-use efficiency (NUE) between pilot and control management zones in 2010
Field
Zone
-1
Fertilizer N (kg ha )
Pilot
Control
% Diff.
-1
Seed N (kg ha )
Pilot
Control
% Diff.
Pilot
NUE
Control
% Diff.
Eleftherio
Eleftherio
A
B
72
45
130
130
-45
-65
49
44
54,1
51,8
-10
-16
0,68
0,98
0,42
0,40
62
145
Gyrtoni1
Gyrtoni1
Gyrtoni1
A
B
C
35
25
35
129
129
129
-73
-81
-73
42
42
39
44,6
44,0
41,2
-5
-6
-5
1,21
1,66
1,11
0,35
0,34
0,32
246
388
247
Gentiki
Gentiki
Gentiki
A
B
C
75
75
53
110
110
110
-32
-32
-52
62
58
52
66,8
65,0
54,2
-6
-11
-5
0,84
0,77
0,98
0,61
0,59
0,49
38
31
100
Table 10. Comparison of fertilizer application and nitrogen-use efficiency (NUE) between pilot and control management zones in 2011
Field
Zone
Fertilizer N (kg ha-1)
Pilot
Control
% Diff.
Seed N (kg ha-1)
Pilot
Control
% Diff.
Pilot
NUE
Control
% Diff.
Gyrtoni2
Gyrtoni2
A
B
118
103
158
158
-25
-35
74
76
82a
93a
-11
-18
0.62
0.74
0.52
0.59
20
26
Omorfo
Omorfo
Omorfo
A
B
C
118
101
111
181
181
181
-35
-44
-39
68
73
79
63
64
60
9
15
31
0.58
0.73
0.72
0.35
0.35
0.33
68
106
115
Gentiki
Gentiki
Gentiki
A
B
C
126
115
121
179
179
179
-29
-35
-32
47
59
54
40
53
55
17
11
0
0.37
0.51
0.45
0.23
0.30
0.31
66
72
45
a - small sample size
27
28
Gentiki 2010
1.3
1.2
Sensor CI
1.1
1.0
B
0.9
A
C
0.8
0.7
0.6
60
80
100
120
140
Days after planting
Gyrtoni 2010
1.4
1.2
AB
C
Sensor CI
1.0
0.8
0.6
0.4
0.2
0.0
60
70
80
90
100
110
120
130
140
150
Days after planting
Eleftherio 2010
1.4
1.2
Sensor CI
1.0
0.8
0.6
A
0.4
B
0.2
0.0
60
80
100
120
140
Days after planting
Fig. 11 Canopy Chlorophyll Index (CI) during the 2010 growing season by management zone
(A, B, C) in each pilot area. Arrows indicate the timing of fertilizer applications.
29
Gentiki 2011
1.4
1.2
Sensor CI
1.0
0.8
B
0.6
A
C
0.4
0.2
0.0
40
50
60
70
80
90
100
110
100
110
Days after planting
Omorfohori 2011
1.4
1.2
1.0
Sensor CI
BC
0.8
A
0.6
0.4
0.2
0.0
40
50
60
70
80
90
Days after planting
Gyrtoni 2011
1.4
1.2
Sensor CI
1.0
B
A
0.8
0.6
0.4
0.2
0.0
40
50
60
70
80
90
100
110
Days after planting
Fig. 12 Canopy Chlorophyll Index (CI) during the 2011 growing season by management zone
(A, B, C) in each pilot area. Arrows indicate the timing of fertilizer applications.
30
31
Eleftherio 2010
800
800
600
600
Count
Count
71-73 DAP
Gentiki 2010
400
200
400
200
0
0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.0
1.4
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Gyrtoni1 2010
Chlorophyll Index
Chlorophyll Index
800
1200
1000
800
600
800
600
400
Count
Count
Count
86-90 DAP
1000
600
400
400
200
200
0
0.0
0.2
0.4
0.6
0.8
1.0
1.2
200
0
0
1.4
0.2
0.4
Chlorophyll Index
0.8
1.0
1.2
0.2
1.4
0.4
0.6
0.8
1.0
1.2
1.4
Chlorophyll Index
Chlorophyll Index
1000
1800
800
1600
800
1400
600
1200
600
400
1000
Count
Count
Count
110-115 DAP
0.6
800
400
600
200
200
400
200
0
0.0
0.2
0.4
0.6
0.8
Chlorophyll Index
1.0
1.2
1.4
0
0
0.2
0.4
0.6
0.8
1.0
1.2
Chlorophyll Index
1.4
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Chlorophyll Index
Figure 13. Histogram comparisons of the distribution of Chlorophyll Index [(NIR/Red edge)-1] by pilot area and days after planting in 2010.
32
Gentiki 2011
Gyrtoni 2011
Omorfohori 2011
180
2500
1000
2000
800
1500
600
140
120
100
80
Count
Count
Count
57-61 DAP
160
1000
400
500
200
60
40
20
0
0
0
0.2
0.4
0.6
0.8
1.0
1.2
0.0
1.4
0.2
0.4
0.6
0.8
1.0
1.2
0.0
1.4
300
250
0.2
0.4
0.6
0.8
0.6
0.8
1.0
1.2
1.4
1000
800
200
600
Count
Count
150
Count
72-76 DAP
250
200
150
400
100
100
200
50
50
0
0
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0.2
0.4
0.6
0.8
1.0
1.2
0.0
1.4
0.2
0.4
1.0
1.2
1.4
Chlorophyll Index
600
700
600
500
Count
400
Count
83-89 DAP
500
300
400
300
200
200
100
100
0
0
0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.4
0.6
0.8
1.0
1.2
1.4
1.4
Chlorophyll Index
Chlorophyll Index
Figure 14. Histogram comparisons of the distribution of Chlorophyll Index [(NIR/Red edge)-1] by pilot area and days after planting in 2011.
33
Benaki Phytopathological Institute
Gentiki 2010
200
Cumulative kg N ha
-1
150
Control
100
Zone A, B
50
Zone C
0
0
20
40
60
80
100
120
140
Days after planting
Gyrtoni 2010
200
Cumulative kg N ha
-1
150
Control
100
50
Zone A, C
Zone B
0
0
20
40
60
80
100
120
140
Days after planting
Eleftherio 2010
200
Cumulative kg N ha
-1
150
Control
100
Zone A
50
Zone B
0
-40
-20
0
20
40
60
80
100
120
140
Days after planting
Fig. 15. Timing and rates of fertilizer N applied in the pilot zones and the controls relative to
crop N uptake (shaded in gray) in 2010. The crop N uptake curve was estimated from crop
yield at the end of the growing season.
34
Benaki Phytopathological Institute
Gentiki 2011
Cumulative kg N ha
-1
300
200
Control
100
Zone B
Zone A
0
-40
-20
0
20
40
60
80
100
120
140
Days after planting
Omorfohori 2011
Cumulative kg N ha
-1
300
200
Control
Zone A
100
Zone B
0
-40
-20
0
20
40
60
80
100
120
140
Days after planting
Gyrtoni 2011
Cumulative kg N ha
-1
300
200
Control
Zone A
100
Zone B
0
-40
-20
0
20
40
60
80
100
120
140
Days after planting
Fig. 16. Timing and rates of fertilizer N applied in the pilot zones and the controls relative to
crop N uptake (shaded in gray) in 2011. The crop N uptake curve was estimated from crop
yield at the end of the growing season.
35
Benaki Phytopathological Institute
Site-specific pest management
Herbicide application
During the growing periods of 2010 and 2011, for band application of herbicides, individual
nozzles were selected to be on only when they were spraying the cotton line and all the
other were shut. By doing so, there was a band (approximately 35cm) of applied herbicide
that followed the planting line. Comparing to control fields, band pre-emergence
applications at pilot fields led to an average 57% herbicide saving for 2010 and 60% for 2011.
During 2010 experimental year, in Gyrtoni and Gentiki sites there were few weeds left after
the band application and the first mechanical cultivation which is the common practice for
cotton in the region. Therefore, site-specific glyphosate application with WeedSeeker was
not proven necessary. In contrast, Eleftherio site had a high number of purple nutsedge and
the application of the WeedSeeker resulted in 55% glyphosate savings. Similar to 2010, in
2011 two of the three pilot fields lacked severe weed infestations making WeedSeeker
application unjustified. However, in the third field (Omorfohori) where WeedSeeker did
applied, herbicide savings were raised to 75% comparing to conventional management.
Insecticide application
Insect monitoring was done for major cotton pests in the area,in the three pilot cotton fields
of the project. Major pests are considered the pink bollworm, Pectinophora gossypiella, and
the American bollworm Helicoverpa armigera. The first is a local pest with relatively stable
populations every year, whereas the second has occasional outbreaks. Monitoring was
performed for a total of five insect pests, namely: Pectinophora gossypiella, Helicoverpa
armigera, Agrotis segetu, Agrotis exclamationis, Agrotis litigiosus.
The levels of incidence for the three noctuide pests (Agrotis sp.) are unknown in the area.
Monitoring was based on trap captures of pheromone traps, a preferable method for these
pests. Delta traps were used for P. gossypiella and funnel traps for the rest of the species.
One trap per species was used in each field so three traps per species in total for all pilot
areas.
In the three pilot sites, for both 2010 and 2011 growing seasons, all pesticide applications for
the most important pest (P. gossypiella) were done after monitoring the adult insects in the
traps and estimation of the ball damage in the crop. In all locations there were no catches of
S. littoralis, so there is no evidence for the presence of this pest in the widely area.
Pesticide Use Efficiency
Herbicide use efficiency was compared between the pilot and the control areas because
there was no differentiation on herbicide sprayings between management zones. Data
showed that the weed management strategies (band applications, use of the WeedSeeker
spraying system) resulted in significant higher use efficiencies in the pilot areas.
36
Benaki Phytopathological Institute
Band applications of herbicides result in significant herbicide savings and provide chemical
weed control only in the area where competition of weeds to the crop is necessary to be
retained. According to the literature, herbicide applications using special systems with
sensors (such as the WeedSeeker system) in the directed-post emergent applications with
hoods, herbicide savings were ranged from 40-70%.
During the 2010 growing season, total amount of herbicides used was 57, 59, and 54% lower
in the pilot areas compared to the control areas for Eleftherio, Gyrtoni1 and Gentiki,
respectively. As a result, the total HUE was 104, 131, and 101% lower in the control areas
compared to the pilot areas for Eleftherio, Gyrtoni1 and Gentiki, respectively. For Elefterio,
Gyrtoni1 and Gentiki the Band-HUE was 113, 131, and 101% lower in the control areas
compared to the pilot areas, respectively. For Elefterio, the WeedSeeker-HUE was 94% lower
in the control areas compared to the pilot area.
During the 2011 growing season, total amount of herbicides used was 60, 68, and 60% lower
in the pilot areas compared to the control areas for Gyrtoni2, Omorfohori and Gentiki,
respectively. As a result, the HUE was 114, 268, and 171 lower in the control areas compared
to the pilot areas for Gyrtoni2, Omorfohori and Gentiki, respectively. For Gyrtoni2,
Omorfohori and Gentiki, the B-HUE was 114, 195, and 171% lower in the control areas
compared to the pilot areas, respectively. For Omorfohori the WeedSeeker-HUE was 372
lower in the control areas compared to the pilot areas, respectively.
Digital weed mapping
The objective was to spatially record and map weed density in cotton fields with the
combination of four different methodological approaches as follows: traditional field work,
the use of RGB field photographs, of multispectral ground-based sensors and on the analysis
of high resolution satellite images.
Traditional field work. The actual state of weed distribution identifying and counting the
existing weed species in situ was done in preselected ground points inside the borders of the
pilot farm. The selection of these points was primarily based on a macroscopic observation
of weed occurrence in the study field and secondly on a spatially interpolated map of NDVI
values, which was derived from the groundbased sensors’ readings produced earlier in June
2011. The number of the predefined points were selected in order to fully cover the classes
of NDVI values produced by interpolating proximal sensors’ readings. In each point,
monitoring was done on 9 frames (0.5 x 0.5 m each) on the ground between four adjacent
rows of cotton. This is a very laborious work but a necessary procedure for validation of the
other 3 methods
RGB field photographs. All aforementioned frames and weeds contained in them, for each
control point were photographed with a digital camera, which was mount on a tripod at 1.0
m distance from ground level. Image processing was carried out on a PC with MATLAB
software. A specific image analysis algorithm was developed for the automatic calculation of
weed cover percentage contained in each photograph. In this methodology, field work was
significantly reduced and by using the specific algorithm, sound estimations of weed density
was achieved. However, this procedure is not a real-time one since it requires a lot of
processing work.
Multispectral sensor readings. Weed monitoring between cotton rows was done using
multispectral sensors connected to dGPS so that the output readings were georeferenced
37
Benaki Phytopathological Institute
reflectance measurements in red, near-infrared, red-edge bands and also spatial referenced
values of the spectral vegetation indices red-edge NDVI and NDVI. During the growing period
of 2012, a new set of field sites were deployed in order to validate this method as compared
with the other previous two methods.
The use of proximal groundbased multispectral sensors in weed monitoring, which is based
in the relation between reflection measurements and spectral signatures of weed flora, is
proved to retain the advantage of quick and reliable estimation of weed presence across a
cotton field. This technology as long as it is used in combination with field work, which
ultimately verifies its results, can offer a significant solution to digital weed management.
The most important weakness of this procedure comes from the fact that there is no
estimation of the weed presence just in the sowing line but only through the closest
proximal points from points within crop lines.
Remote sensing data. In HydroSense, we used resolution merge techniques (pansharpening)
to produce high resolution, multispectral imagery. In this way, the interpretability of the
data is improved by having high resolution information which is also in colour. This
methodology has got the clear advantage of estimations of weed presence at a larger scale
(watershed level). However, it shows potential use at field level particularly in crops such as
cotton where the sowing lines are 98cm giving enough space for proper weed detection.
Requirements for site-specific pest management
Collecting data. Remote sensing is the term used to describe the acquisition of information
from far away locations such as satellite-based and aircraft-based sensors. The advantage of
using multispectral images collected in this way is that large areas can be rapidly and
repeatedly covered throughout a growing season. In general, by examining the reflectance
values at certain regions of the electromagnetic spectrum it is possible to differentiate
various targets like crops, weeds and soil. For monitoring weed and pest outbreaks a very
high resolution (≤ 50 cm) is rather needed. The cost of purchasing imagery over a desired
area is usually dependent upon the spatial resolution and as far as weed identification and
mapping is concerned, where precision is needed, the cost is high and thus prohibitive for an
average producer in Greece.
Pesticides and herbicides are the two of the most expensive controllable costs in cotton
production. Remote sensing technology and image processing analyses offer a potential to
diminish these expenses. Mapping weeds using multispectral imagery prior to crop
emergence or between crop rows is relatively straightforward as in most cases weeds are
not randomly distributed across a field but are grown in patches. These weed patches can be
detected by the presence of biomass in contrast to bare soil spectral reflectance. The
calculation of weed cover through image analysis techniques is rather easy and is dependent
on the image resolution and hence on/off prescription maps for spraying weeds with a
minimum amount of herbicide is a true perspective. On the contrary, species identification,
or discrimination between weeds and crop plants is a difficult task. The experience from the
project indicates that with the use of multispectral imaging this kind of procedure in not
applicable and that hyperspectral imaging could be of great help towards this direction.
Under the same notion, pest and disease infestations can be detected with remote sensing
techniques as long as they alter leaf chlorophyll, leaf area index or biomass. On time
recognition of infested areas can lead to early zone spraying, eliminating insects with
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minimal amount of pesticides. However, it should be noted that the mobility of insects
makes their site-specific management much more difficult than that of weeds. In both weed
and insect monitoring of great importance is the temporal factor, meaning that data
collection should be implemented at the right time window, which is defined by the balance
between the reliability of data leading to proper decisions and the timely but successful
treatment of the pest problem. Certain limitations of remote sensing techniques include
atmospheric conditions, time scheduling and costs. However, this technology is rapidly
evolved and will soon overcome present burdens.
With the use of ground based sensors (multispectral sensors and RGB cameras), in
Hydrosense project the opportunity of automating the collection of weed cover data was
given, at a spatial resolution that is not economically feasible with manual sampling
methods. Indeed, increasing the intensity of sampling results in a more accurate
characterization of within-field variability. Towards increasing samples, the use of the
multispectral sensor in relation to the camera is preferable because the user simply runs the
selected scan area and his speed can refine the number of measurements. Capturing data
with the camera, presupposes the choice of shooting points, going over to each one,
positioning the camera system (tripod) and taking the picture. This process greatly increases
measurement time and accordingly the user’s fatigue. To expedite shooting procedures, a
video file could have been produced and further split into individual images or a
corresponding aerial photograph could have been purchased. In terms of time, the number
of records that a user can take with the multispectral sensor is higher than the according
number of photos with the camera. Besides, increasing the number of records optimizes the
spatial coverage of the interest area and thereby one can obtain a more representative
picture of the weed flora in the region. Consequently, according to the work of the year
2012, the method of weed mapping using multispectral sensor is safer in terms of reliability,
compared to taking photos. An aerial photo of the study area (e.g. by a camera mounted on
a remote controlled airplane or helicopter) is expected to reduce time for raw data
recording.Indeed these methods minimize measurement time and offer large scale results
by capturing a large area with one shot, however they introduce increased costs and post
processing office work. The purchase of equipment including the multispectral sensor, the
data logger and the GPS, is quite expensive (according to current prices) and therefore
unattractive to the average Greek producer. Instead, the camera system is cheaper and
much easier to learn and use.
Analysing data. Once data on weed spatial variability have been collected, it is necessary to
establish a decision support tool to visualize the significant in field variation and to suggest
the imminent management solutions. A means towards decision support in Precision
Agriculture is Geographical Information Systems (GIS). GIS are systems, consisting of
hardware and software, through which the user can acquire, store, retrieve, manage,
analyse and map spatial and tabular data. What makes GIS a powerful decision support
system is the expert knowledge that ought to integrate. Indeed, the integration of various
data sources with expert knowledge and other decision models, can aid producers in taking
strategic decisions for both short and long term. The cost of purchasing a GIS package is an
issue for most Greek produces, and when this is overcome, the producer should prior be
trained in how to use it and in fully developing its capabilities. A more realistic scenario
under Greek farming conditions would be that of external servicing and agronomic
consultation. Such services would undertake the data collection and analysis via GIS
procedures, on behalf of the producer, and would be responsible for decision and strategic
planning in farm and producer level.
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Upscaling data. The usage of weed data collected at field scale towards upscaling them at
watershed level could be realized if a link between those sources of information was
established. Under a reliable and substantiated link, which depends on the number of initial
observations and their distribution in space, an upscaling through extrapolation procedure
could be accomplished. More specifically, actions that could be undertaken through the
upscaling of field data can be summarized as follows:
1. Defining the spectral signature of representative weeds, soils and crops in the pilot
fields could be used to classify a satellite image covering the watershed area. As
prerequisitesare the selection of unmixed and individual land cover types as
reference areas, the verification of image classification results with field work and
the determination of reliable and site specific reflectance value limits for each land
cover.
2. The correlation of weed presence with soil and/or climate characteristics for which
there is data availability at a watershed level, could serve as an indirect approach for
defining wider weed flora zones, or risk zones susceptible to certain weed species
infestations.
3. The information collected as part of the configuration of baseline status in the
project’s area, through the deployment of questionnaires, is invaluable for decoding
and depicting the importance of specific species grown in the area at a larger area.
This could be strengthened by historical records found for the study area from
secondary sources.
Conclusions – Management decisions
Weed management consists of a crucial and demanding agronomic process prior and during
a cultivation period. Weeds among others factors can seriously diminish yield and degrade
the quality of final product through competition phenomena, hosting and spreading diseases
and enemies, preventing harvesting etc. Weed incidents are not homogenous across fields.
In fact weed populations generally grow in patches; however producers tend to apply
uniform rates of agrochemicals across their fields. This practice suggests unjustified use of
pesticides and implies extra costs and environmental burdens. The key point in adapting and
balancing inputs according to weed locations is the integrated monitoring of weed flora in
terms of time and space.
The methodology we used gave sufficient results as far as the management of weeds and
insects in a cotton field are concerned. The insecticide methodology was based on the classic
method of monitoring insects’ population through captures of pheromone traps. It
constitutes a rather simple and efficient method. The advantages of this method are that the
cost of trap establishment is low and no particular training of farmers is required. Finally it
can ensure high effectiveness depressing the application cost. Generally, the specific
conditions within each area and the presence of beneficial insects determine the balance
between the insect population and level of damage on cotton production. In any case proper
monitoring and timely communication of the observed situation would enable proper
management decisions for pest control.
Band applications of herbicides result in significant herbicide savings and provide chemical
weed control only in the area where competition of weeds to the crop is necessary to be
retained. In addition, herbicide applications using cutting-edge systems with sensors in the
case of directed-post emergent applications with hoods, herbicide savings were ranged from
40-70%, which was accompanying with no significant reduction in cotton yield. However, the
use of this method in a farmer’s level may have some difficulties. It is more than obvious
that special training is required. To test effectively and to implement site-specific
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Benaki Phytopathological Institute
applications on a large scale, a sprayer suited for precisely targeting weed patches in the
field is necessary. Given that cotton is susceptible to glyphosate applications over the top,
the spraying system should have specially constructed hoods that retain the substance and
therefore no phytotoxicity will be appeared. On the other hand the cost of such equipment
is high for an average Greek cotton producer mainly because of the small farms (about 4
ha/farmer). However, an investment in a cooperative level could be a realistic solution.
Parameters related to the development of regional GIS database
Introduction
The goal of this study was to detect correlations between field properties of the
demonstration sites monitored during the growing season (ground truthing ) and radiance
values of satellite imagery. These correlations need to apply not only within fields, but also
across fields, in order to project them to the entire Pinios watershed and thereby provide
any meaningful information to the GIS database. Exploratory correlation and factor analysis
indicated that only few across field correlations were obtained between soil - plant variables
and the wavebands of the satellite imagery. The lack of consistent correlations between any
two variables across fields should not be surprising because of intrinsic variation in soil
properties and management practices, i.e., different histories of fertilization and different
soil types. Other interfering factors that made our goal difficult to achieve were a) the
variable-rate inputs that altered field properties during the growing season and, b) the
difficulty to exactly align the coordinates of canopy measurements of the multispectral
sensors to those of the fixed sampling positions. The few significant across-field correlations
and their importance in the GIS database are the subject of this report.
Methodology
Composite soil and plant samples from four sampling locations in each management zone
were taken several times during the growing season and analyzed in the laboratory. The
exact position of each sampling location was recorded with a GPS receiver that received
differential signal (Omnistar BV, Leidschendam, NL) with an accuracy of <0.5 m. Standard soil
quality analysis included water content (w/w), pH and electrical conductivity (EC) adjusted to
a 1:1 soil-water ratio. Nitrate content was determined colorimetrically (FIAstar 5000 analyzer
by Foss) in soil extracts of 1M potassium chloride. Carbonate content, as an estimate of
inorganic C, was determined by using a Bernard calcimeter to measure the released CO2 after
addition to soil of dilute HCl solution. Total nitrogen and total carbon content, as well as
isotopic composition (δ15N, δ13C) of soil and leaf samples, were measured by an automated
combustion elemental analyzer interfaced with a triple collector isotope ratio mass
spectrometer (IRMS, PDZ Europa, Crewe, UK). Soil organic matter was determined by the
Walkley-Black method of wet oxidation. Soil texture was determined by physical
fractionation.
A World View 2 multispectral image was taken on March 23, 2010 covered an area of 122
km2 that included the HydroSense demonstration sites. At that time the soils were already
plowed to a depth of 30 cm with unknown soil water content. The four spectral bands (blue
at 442-515 nm, green at 506-586 nm, red at 624-694 nm and NIR at 765-901 nm) of the
image were processed at a spatial resolution of 2.5 m in multispectral mode (0.5 m in
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panchromatic mode). Radiance values of surface reflectance of the pixels corresponding to
each sampling location were recorded after the data was corrected for solar irradiance and
atmospheric effects.
Factor analysis, linear correlation and regression were used to quantify the strength of the
relationship of soil and plant properties with image radiance values of the four spectral
bands. The Statistical Analysis System (SAS Institute, 1990) was used for all data analyses.
Results and conclusions
Several correlations were obtained between soil-plant variables and pixel radiance values of
satellite imagery within fields, but much fewer across fields. The lack of consistent
correlations between any two variables across fields should not be surprising because of
intrinsic variation in soil properties and management practices, i.e., different histories of
fertilization and different soil types.
However, significant correlations were also obtained across fields between pixel radiance
values of satellite imagery and soil N, C, electrical conductivity, water content and texture.
The highest (negative) correlation was obtained between satellite NIR reflectance and soil N
content (Fig. 17). Our results indicate that NIR reflectance of high resolution satellite
imagery was not only able to quantify within-field variability in total soil N, but also acrossfield variability in a single regression equation. This equation was used to predict the spatial
distribution of soil organic matter content across a large section of the Thessaly Plain
(included in the GIS maps provided in Action 5) and to provide a decision support tool for
site-specific management. It is expected that the importance of these maps will be verified
in future case studies for soil quality modeling at the field and regional scale.
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140
y = 184 - 0.35x + 2.2x10-4x2
R2 = 0.77
-1
Total soil N (mg kg )
120
100
80
60
40
20
200
400
600
800
Radiance of NIR band
Figure 17. The negative relationship between satellite NIR reflectance and total soil N at the
5-30 cm depth across the three fields (circle: Gyrtoni, square: Omorfohori, triangle:
Eleftherio).
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Benaki Phytopathological Institute
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