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Transcript of An Approach for Precision Farming Under Pivot Irrigation System Using Remote Sensing and GIS...
An approach for precision farming under pivot irrigation system using remote sensing and GIS techniquesA.H. El Nahrya, , , R.R. Alib and A.A. El Baroudyc
a National Authority for remote Sensing and Space Science, 23 Joseph Tito Street, El-Nozha El-Gedida, Cairo, Egyptb Soils and Water use Department, National Research Centre, Cairo, Egyptc Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta, Egypt
Received 25 April 2010;
accepted 19 September 2010.
Available online 11 November 2010.
AbstractThe current work is aimed to realizing land and water use efficiency and determining the profitability of precision
farming economically and environmentally. The studied area is represented by an experimental pivot irrigation field
cultivated with maize in Ismailia province, Egypt. Two field practices were carried out during the successive summer
growing seasons (2008 and 2009) to study the response of maize plants single hybrid 10 (S.H.10) to traditional and
precision farming practices. Traditional farming (TF) as handled by the farm workers were observed and noted
carefully. On the other hand precision farming (PF) practices included field scouting, grid soil sampling, variable rate
technology and its applications. After applying PF a dramatic change in management zones was noticed and three
management zones (of total four) were merged to be more homogenous representing 84.3% of the pivot irrigation
field.
Under PF Remote Sensing and Geographic Information System techniques have played a vital role in the variable
rate applications that were defined due to management zones requirements. Fertilizers were added in variable rates,
so that rationalization of fertilizers saved 23.566 tonnes/experimental pivot area. Natural drainage system was
improved by designing vertical holes to break down massive soil layers and to leach excessive salts. Crop water
requirements were determined in variable rate according to the actual plant requirements using SEBAL model with
the aid of FAO Cropwat model. Irrigation schedule of maize was adopted considering soil water retention, depletion,
gross and net irrigation saving an amount of water equal to 93,718 m3 in the pivot irrigation field (153.79 acre).
However costs of applying PF were much higher than TF, the economic profitability (returns-costs) achieved
remarkable increase of 29.89% as a result of crop yield increment by 1000, 2100, 800 and 200 kg/acre in the
management zones 1, 2, 3 and 4, respectively. Finally applying adequate amounts of fertilizers beside water control
the environmental hazards was reduced to the acceptable limits.
Keywords: Precision farming; SEBAL; Cropwat ; Management zone; Remote sensing and GIS
Article Outline
1.
Introduction
2.
Materials and methods
2.1. Study area
2.2. Remote sensing works2.3. Field practices
2.3.1. Crop scouting
2.3.2. Grid soil sampling
2.3.3. Global positioning system (GPS)
2.4. Laboratory work
2.5. Spatial variability of soil characteristics2.6. Variable rate technology and variable rate application
2.6.1. VRA of fertilizers
2.6.2. VRA of compacted saline field spots
2.6.3. VRA of water consumption use
2.6.3.1. SEBAL model (based on satellite imagery)
2.6.3.1.1. Retrieving land surface temperature (LST)
2.6.3.1.2. SEBAL and evapotranspiration
2.6.3.2. Cropwat model (based on FAO Penman–Monteith approach)
2.6.3.3. Climatic, crop and soil data for cropwat
2.7. Yield mapping
2.8. Fertilizers application recommendation
3.
Results and discussions3.1. First season practices of TF (2008)
3.1.1. Management zones
3.1.2. Analyzing the traditional/common practices
3.1.3. Soil characteristics3.2. Second season practices of PF (2009)
3.2.1. PF field scouting
3.2.2. Grid soil sampling
3.2.3. Soil mapping based on VRT
3.2.4. Variable rate technology/application (VRT/VRA)
3.2.4.1. VRA of fertilizers
3.2.4.2. VRA of compacted saline field spots
3.2.4.3. VRA of water consumption use
3.2.4.3.1. SEBAL model (based on satellite images)
3.2.4.3.2. Cropwat model (based on FAO Penman–Monteith approach)
3.2.4.3.3. Crop water requirement (CWR)
3.2.4.3.4. Irrigation schedule of maize
3.2.4.3.5. Soil water retention
3.2.5. Correlation analysis of NDVI vs. maize yield
3.2.6. Change detection of management zones and yield
3.3. Precision farming profitability
3.3.1. Economic profitability
3.3.2. Environmental profitability
4.
Conclusions
Recommendations
References
1. IntroductionAgricultural production has experienced dramatic changes during the past few decades.
Traditionally, farming practices have assumed that fields are homogeneous in nature, and
management practices seek to determine input application rates based on what is best for the
field as a whole (Isik and Khanna, 2003 ). Under traditional farming (TF), the physical and
chemical properties of the soil determined from manual soil sampling are often used as a base
to recommend fertilizer for crops. Normally a large number of samples, and hence large
expense in cost and time, is needed to achieve statistical significance among samples in
determining management zones (Franzen et al., 2002 ). Till now only a small percentage of
farmers actively seek out new technologies and apply them. These technologies presented
what is called Precision Farming (PF). It is a management strategy that uses information
technologies to derive data from multiple sources to bear on decisions associated with crop
production (National Research Council, 1997). It involves studying and managing variations
within fields that can affect crop yield. It also involves the sampling, mapping, analysis, and
management of specific areas within fields in recognition of spatial and temporal variability with
respect to soil fertility, pest population, and crop characteristics ([Weiss, 1996] and [ Nemenyi
et al., 2003]). PF is concerned with the ability to vary rates of application and precisely apply
inputs based on actual crop needs (Zhang et al., 2010). Developing a management zone map
under PF is essential for effective variable rate applications. To develop a zone map, normally
three factors should be considered i.e. information to be used as a basis for creating zones,
procedure to be used to process the information, and the optimal number of zones that a field
should be divided into (Fridgen et al., 2004 ). Efficient and easy-to-use tools that address all
these factors are required to provide a technology delivery mechanism (Zhang et al.,
2002). Fleming et al. (2000) evaluated farmer-developed management zone maps and
concluded that soil color from aerial photographs, topography, as well as the farmer's past
management experience are effective in developing variable rate application maps. Remote
sensing is very important in PF where its usage is based on the relationships of surface
spectral reflectance with various soil properties and crop characteristics (Moran et al., 1997).
Multi-temporal images within a growing season of some field crops have also been used to
study within-field variability (Begue et al., 2008 ). Spectral reflectance of the soil or crops that
were measured in the laboratory (Daniel et al., 2004), from field spectrometer (Read et al.,
2002), from air and space born imagery ([Fleming et al., 2000], [ Seelan et al.,
2003] and [Sullivan et al., 2005]) have been widely used in developing variable rate application
maps. Spatial imagery in agriculture has been used for crop management since 1929 when
aerial photography was used to map soil resources (Seelan et al., 2003 ). Despite these
theoretical advances and successful applications, access to and use of remote sensing data
by end users require considerable technical knowledge about computing and remote sensing
is still a challenge (Moreenthaler et al., 2003 ). An unsupervised classification algorithm has
been shown to be effective in delineating a field into management zones for a variety of
applications (Lark and Stafford, 1997). Determining the most appropriate number of zones is
difficult in the interpretation of unsupervised classification, so normalized differences
vegetation index (NDVI) was used in the current work. Spatial variability in yields has been
considered as another useful indicator in determining variable rate nutrient management
(Johnson et al., 2003). The yield variation not only reflects variation of potential soil productivity
but also provides an indication of the nutrient level for the following season if crop residues are
left to decay (Brock et al., 2005). Finally it is worthy to say PF could be considered as an
integrated crop management system that attempts to match the kind and amount of inputs with
the actual crop needs for small areas within a farm field. It provides tools for tailoring
production inputs to specific zones within a field, thus to achieve PF, constraints that preclude
its application should be identified and adequate management practices on the management
zone level should be adopted.
The current work aimed to realize land and water use efficiency and to determine the
profitability of precision farming economically and environmentally.
2. Materials and methods
2.1. Study areaThe studied area was represented by an experimental pivot irrigation field at the Sixth of
October Company for agricultural projects, El-Salhia area, which is located to the south west
of Ismailia governorate, Egypt. It is bounded by 30°24′02″ and 30°32′16″ latitudes and
31°57′36″ and 32°03′06″ longitudes as shown in Fig. 1.
Full-size image (66K)
Fig. 1.
Location of the study area.
2.2. Remote sensing worksA total of seven cloud free landsat enhanced thematic mapper (ETM+) satellite images were
used, one used at the high peak of growing season (July 2008) to identify the management
zones through deriving NDVI and six others acquired on May 28, June 13, June 29, July 15,
July 31 and August 16, 2009 were used to generate ETc maps for summer maize single
hybrid 10 (S.H.10) in the 2009 growing season.
Digital image processing for Landsat ETM+ satellite images with spatial resolutions of
28.50 m acquired years 2008 and 2009 was executed using ENVI 4.7 software (ITT, 2009).
Digital image processing included gap-filling of ETM+ SLC-off images in which all missing
image pixels in the original SLC-off image have been replaced with estimated values based
on histogram-matched scenes. Data were calibrated to radiance using the inputs of image
type, acquisition date and time. Images were stretched using linear 2%, smoothly filtered,
and their histograms were matched according to Lillesand and Kiefer (2007) .
Images were atmospherically corrected using FLAASH module (ITT, 2009). Satellite images
were rectified (radiometrically and geometrically). Reflectance bands (red and near infrared)
and radiance ones (thermal infrared) of ETM+ images were used to derive different surface
parameters such as NDVI, surface albedo, surface emissivity and surface temperature. The
bands and sensor characteristics of Landsat 7 ETM+ are presented in Table 1.
Table 1. Characteristics of visible and infrared bands of Landsat 7 ETM+.
Sensor typeBand No.
Spectral resolution
Spatial resolution
Temporary resolution
Radiometric resolution
Landsat 7 3 R (0.630–0.690) 28.5 16 days 8 bit
Sensor typeBand No.
Spectral resolution
Spatial resolution
Temporary resolution
Radiometric resolution
ETM+4 NIR(0.7500.900) 28.5 16 days 8 bit
6.1TIR(10.400–12.50)
57 16 days –
2.3. Field practicesTwo field practices were carried out at the experimental pivot irrigation field during
successive summer growing seasons (2008 and 2009) to study the response of maize plants
single hybrid 10 (S.H.10) to TF (year 2008) and PF (year 2009).
TF practices that were applied by the farm producers (year 2008) consisted of the following:
In summer 2008, soil of the investigated area was ploughed after wheat. Nitrogen was added
at 160 kg N/acre as urea, phosphorous was added at 60 kg P2O5/acre as single super
phosphate, and potassium was added at 60 kg K2O/acre as potassium sulphate. Fertilizers
were applied under pivot irrigation system at the same quantity across the field, before
sowing. Row spacing was 0.50 m. Maize was sown on 15 May. Plants were harvested on 16
August. During this period, the plant growth and field conditions were observed accurately
day by day for recognizing the effect of traditional farming on maize growth and yield.
PF practices were applied during 2009 under full control of the investigators is as follows.
2.3.1. Crop scouting
Crop scouting encompassed periodic ground-level inspection of the crop development. Basic
field scouting equipment included: a clipboard with field scouting forms, field maps, a shovel,
a pocket knife, plastic and paper bags for collecting samples, a 10× hand lens and a
sampling frame, satellite images, a high resolution camera, labels for identification, HCl and
a GPS hand held unit to mark the locations.
2.3.2. Grid soil sampling
A detailed survey was conducted to establish the field condition. Samples were collected
using a soil auger and spade with properly labeled bags. Collection of soil samples was
carried out based on a systematic grid layout across a farmed field. The field was sampled
on a 2 × 2 second grid (30.5 m × 30.5 m) with a total of 68 sampling location points.
Sampling depth was 0–0.30 m. A sampling grid was obtained from an ARC map module
(ESRI, 2008). Four to 18 sub samples were taken from the top 0.30 m of soil to create a
composite sample.
2.3.3. Global positioning system (GPS)
A GPS hand held unit for field scouting was used to determine precise location (latitude and
longitude in UTM units) based on radio signals from 4 or more of the 24 satellites in the GPS
system.
2.4. Laboratory workSoil samples obtained from the field were used for the determination of electric conductivity
(EC dS/m), soil pH (1:2.5 abstract), organic matter %, CaCO3%, and macro and micro
nutrients (mg/kg) according to Bandyopadhyay (2007).
2.5. Spatial variability of soil characteristicsAn interpolation method was used to visually identify the spatial variability and mapping soil
characteristics. Interpolation between sampling locations was made as ordinary kriging
interpolation method performed using the geostatistical analyst extension available in ESRI©
ArcMap™ v9.3 (ESRI, 2008).
2.6. Variable rate technology and variable rate applicationVariable rate technology (VRT) and variable rate application (VRA) that were considered as
the backbone of PF were applied, so that the practice of whole-field application of chemicals
has been replaced by site-specific treatments, sprayers that were capable of variable rate
applications were essential. These machines were programmed to deliver precisely the right
amount of fertilizers to the pivot irrigated field. Variable rate technology was essentially used
to allow variable rates of fertilizer application, irrigation scheduling and tillage throughout the
pivot irrigation field. The rate was changed due to a preset map or through information
gathered by satellite sensors. VRT was used in conjunction with mapping information (map
based VRT) such as yield maps and soil characteristics maps. Components of variable rate
technology that were used in the studied pivot irrigation field were: 1, computer and controller
(integrated into one product); 2, DGPS (Differential GPS); 3, hydraulic valve and motor and
4, metering device. All these components were attached to the tractor and combine
harvester.
2.6.1. VRA of fertilizers
The fertilizers information was interpreted by computer. A controller was used to increase or
decrease the amount of input due to the application maps. The obtained information was
combined with regular field survey, accurate identification, diagnosis of problems and a
record of those observations for a successful crop management program. The information
obtained from field scouting was used to determine if any immediate actions should be taken
as well as future reference to avoid problems in subsequent years. Every location in the field
was evaluated to its specific characteristics and assigned an optimal input application rate
unique to that location.
2.6.2. VRA of compacted saline field spots
Affected spots that were identified by field scouting; remote sensing and GPS were treated
as follows:
Subsurface ploughing was executed to break the subsurface shale hard pans (40 cm depth)
as well as the massive layers of shale. After breaking the patches of hard pans, vertical
drainage holes were excavated with diameter of 1.0 m and 2.4 m depth to encourage natural
drainage by gravity. The distance between the adjacent drainage holes was 15 m. These
vertical drainage holes were subdivided into 4 layers (a, b, c and d) and managed from
bottom to top as follows: Hole bed was filled with stones (diameters 0.08 m) to a height of
0.60 m, the middle layer was filled with gravel (diameters 0.05 m) to a height of 0.60 m, the
subsurface layer was filled with finer gravels (diameter 0.03 m) to a height of 0.60 m and
finally the surface layer was covered by the original soils. All layers were separated from
each other by straw layers with height of 0.2 m to keep porosity and acting as filters.
2.6.3. VRA of water consumption use
2.6.3.1. SEBAL model (based on satellite imagery)
The surface energy balance algorithm for land (SEBAL) model with the aid of Penman–
Monteith model and remote sensing was used for estimating crop evapotranspiration (ETc)
on the experimental pivot irrigation field scale under local climatic conditions of Ismailia
governorate. All processes have been executed through raster band math module, ENVI
4.7, ITT (2009) as follows.
2.6.3.1.1. Retrieving land surface temperature (LST)
Six enhanced landsat thematic mapper (ETM+) were used (thermal band 6.1) for retrieving
land surface temperature (LST). Sensors acquired temperature data and stored this
information as a digital number (DN) with a range between 0 and 255. DNs were converted
to degrees Celsius using two steps.- The first step was to convert the DNs to radiance values in mW/(m2.sr.0.01 m) using the bias and gain values obtained from image header file.
(1)CVR= G (CV DN)+ B where CVR is the cell value as radiance, CVDN is the cell value digital number, G is the gain and B is the bias (or offset), (
NASA, 2002).
- The second step was to convert the radiance data to degrees in Kelvin as follows:
(2) where T is degrees Kelvin, CVR is the cell value as radiance, K1 is 666.09 and K2 is 1282.71 (
NASA, 2002).
2.6.3.1.2. SEBAL and evapotranspiration
The pre-processing parameters required for surface energy balance algorithm for land
(SEBAL) that were derived from digital image processing included the normalized difference
vegetation index (NDVI), emissivity, broadband surface albedo, and surface temperature.
The NDVI was calculated from bands 3 and 4 of ETM+ image, and the broadband albedo
was calculated using weighing factors of all visible, near infrared and short wave infrared
bands (Liang et al., 1999). Surface emissivity of the sensor was calculated from the derived
NDVI. Surface temperature was calculated from thermal band 6.1. Calculation of the net
incoming radiation and the soil heat flux were done after Bastiaanssen (1995), while the later
development of Tasumi et al. (2000) were incorporated to determine the sensible heat flux.
Temperature difference between air and soil for the “hot” pixel (i.e., where the latent heat flux
is assumed null) was calculated. Air density was obtained by generalizing meteorological
data of relative humidity and maximum air temperature from Ismailia meteorological station
at the time of satellite overpass. The ET was calculated in SEBAL (Hafeez , 2003 ) from the
instantaneous evaporative fraction (Λ) and the daily averaged net radiation (Rn24).
(3)ET24= Λ [ R n24×((2.501−0.002361×LST)×106)] mm×day −1 where ET24 = daily ET actual
(mm × day−1); Rn24 = average daily net radiation (W/m2); and LST = land surface temperature
(°C). The instantaneous evaporative fraction expresses the ratio of the actual to the crop
evaporative demand when the atmospheric moisture conditions are in equilibrium with the
soil moisture conditions. The evaporative fraction tends to be constant during daytime
hours. Λ is computed from the instantaneous surface energy balance at the moment of
satellite overpass for each pixel.
(4) where λE = latent heat flux (the energy allocated for
water evaporation; it describes the amount of energy consumed to maintain a certain crop
evaporation rate). λ can be interpreted in irrigated areas as the ratio of actual evaporation to
crop potential evaporation. It depends upon the atmospheric and soil moisture conditions
equilibrium. Rn = net radiation absorbed or emitted from the earth's surface (radiative heat in
W/m2); G0 = soil heat flux (conduction in W/m2) and H0 = sensible heat flux (convection in
W/m2). The evaporative fraction tends to be constant during daytime hours;
the H0 and λE fluxes, on the contrary, vary considerably. The difference between the Λ at the
moment of satellite overpass and the Λ derived from the 24-h integrated energy balance is
marginal, and may be neglected ([ Brutsaert and Sugita, 1992] , [ Crago, 1996] and [Farah,
2001]). For time scales of 1 day or longer, G0 can be ignored and net available energy
(Rn − G0) reduces to net radiation (Rn). By solving the abovementioned equations integrated
with some weather conditions and water availability in the field, ET24 could be obtained. The
ET24 calculation through remote sensing on specific dates displayed reasonable results of its
spatial distribution in the pivot irrigation system. However, this information could not be used
directly, as ET24 mainly depends upon weather conditions and water availability in the field,
which varies by the hour. It was therefore necessary to simulate daily values to get an
accurate estimation of seasonal ET. A larger sample of timely ET observations is necessary
to obtain an accurate result and to adjust the daily fluctuation of ET24 for integration of
seasonal ET24. As proposed by Tasumi et al. (2000) . Evapotranspiration of maize crop could
be calculated as follows:
(5) where Kc = the single crop coefficient; ETc = actual crop evapotranspiration;
ETo = the reference evapotranspiration, (Bastiaanssen et al., 2000 )
(6)ETc= K c×ET24 (mm×day −1 ) where ETc = crop evapotranspiration (mm day−1), Kc = crop
coefficient (dimensionless) and ETo = reference crop evapotranspiration (mm day−1).
2.6.3.2. Cropwat model (based on FAO Penman–Monteith approach)CROPWAT 8.0 for Windows is a computer program designed to calculate crop water
requirements and irrigation scheme based on soil, climate and crop data (Smith, 1992).
CROPWAT for Windows uses theFAO (1992) Penman–Monteith method for calculations.
The FAO Penman–Monteith method through Cropwat model was used to estimate ETo and
ETc through the following equations:
(7)
(8)ETc=ETo× K cwhere ETo reference evapotranspiration [mm day−1], Rn net radiation at the crop
surface [MJ m−2 day−1], Gsoil heat flux density [MJ m−2 day−1], T mean daily air temperature at
2 m height [°C], U2 wind speed at 2 m height [m s−1], es saturation vapor pressure
[kPa], ea actual vapor pressure [kPa], es − ea saturation vapour pressure deficit [kPa], Δ slope
vapor pressure curve [kPa °C−1], γ psychrometric constant [kPa °C−1],ETccrop
evapotranspiration [mm day−1] and Kc crop coefficient.
2.6.3.3. Climatic, crop and soil data for cropwat
The daily climatic data of the year 2009 were obtained from Ismailia meteorological station
longitude 32°.25 latitude 30°.60 and altitude 13.0, including maximum and minimum air
temperature, relative humidity, wind speed, sunshine duration and rainfall. The Crop data
input included the following compulsory parameters: Planting date, Crop coefficient (Kc),
stages, rooting depth, critical depletion fraction (p) and yield response factor (Ky).Soil data
included total available water (TAW), maximum infiltration rate, maximum rooting depth and
initial soil moisture depletion.
2.7. Yield mappingYield mapping system was used to measure and record the amount of grain being harvested
at any point in the field with the position of the combine harvester. To produce such yield
map, the harvester was equipped with a GPS receiver. Yield data were sent to the onboard
computer where measured yield was matched with its appropriate field position and NDVI
obtained from satellite images.
2.8. Fertilizers application recommendationFertilizers recommendations were calculated by using the soil test fertilizer recommendation
program developed by Mc Vay (2005) .
3. Results and discussionsTwo field trials were carried out in two successive seasons, (2008–2009) as follows:
3.1. First season practices of TF (2008)
3.1.1. Management zones
Regions of similarity or management zones were defined with the aid of NDVI derived from
satellite image. Four management zones (1, 2, 3 and 4) were identified with areas of 3.87,
82.78, 62.31 and 5.83 acre respectively. Fig. 2 shows these management zones.
Full-size image (65K)
Fig. 2.
Pivot management zones.
3.1.2. Analyzing the traditional/common practices
The survey began year 2008 with the recording of vital field information on soil fertility and
crop inputs. As mentioned before, the pivot irrigation field was divided into four zones
depending upon NDVI that is highly correlated with plant growth/health, biomass and yield as
well. Field survey showed the best crop growth at zone 1. This area represented the
relatively low leveled soils, where enough water and excessive fertilizers were accumulated.
The relatively elevated soils (zone 2) had fairly healthy vegetation. The high elevated soils
(zone 3) where topsoil was depleted by wind erosion, showing moisture and nutrients stress.
This stress was reflected negatively on vegetation health and yield accordingly. The plant
growth in zone 4, which was affected by moisture shortage and nutrients depletion, was
severely damaged. Those plants were located on the pivot perimeter, or on soils that were
suffering from subsurface hardpan (shale). Yield was estimated roughly by 2.2, 1.9, 1.6 and
0.8 tonnes/acre for zones 1, 2, 3 and 4 respectively. Observing this magnitude of variation
prompted to ask how the problem could be solved, so using new attitudes was urgent.
3.1.3. Soil characteristics
Soils of the study area were mostly sandy with some gravel on the surface, having a minimal
content of clay and a low nutrient retention capacity. The surface was almost flat to
undulating. There were some patches of shale in the subsurface layer (0.30 m depth)
consisting of very fine clay cemented by gypsum, CaCO3 and iron oxide. Soil structure was
single grains. Stratified layers (Aeolian deposits) were noticed when inspecting and
describing soil profiles reflecting the action of the winds as an important soil formation agent.
Characteristic of the investigated soils in 2008 are illustrated in Table 2. It is noticed that, soil
characteristics were correlated with the results of crop yield, where zone 1 had relatively
adequate soil characteristics represented by low CaCO3 and EC values, fair O.M & pH
values and adequate nutrients level. In zone 4, there was a clear deficiency in both macro
and micro nutrients. On the other hand, there was a shortage in organic matter content and
salinity was high in most samples.
Table 2. Characteristic of the investigated soils.
Soil characteristics
Zone 1. Zone 2 Zone 3 Zone 4
CaCO3% 1.11–2.75 1.5–6.32 1.56–6.63 1.87–10.23Salinity EC dS/m 1.23–2.63 1.14–3.21 1.21–3.15 9.23–10.56pH 1:2.5 extract 7.88–8.17 7.89–8.22 7.89–8.23 7.98–8.42O.M.% 1.12–1.21 0.61–0.79 0.43–0.58 0.12–0.19Macro nutrients mg/kgN 35.78–88.87 16.98–40.0 3.89–33.76 8.00–34.34P 12.67–17.29 7.67–12.45 6.67–11.45 2.0–9.45K 130.20–140.52 18.99–401.52101.52–32048.52–132.54Micro nutrients mg/kgFe 7.83–9.98 3.5–9.09 3.1–4.56 1.0–10.0
Soil characteristics
Zone 1. Zone 2 Zone 3 Zone 4
Mn 2.43–3.86 2.71–6.45 0.71–3.0 0.86–4.71Zn 3.56–5.92 0.43–2.45 1.44–4.83 0.06–2.06Cu 0.75–1.31 0.33–0.99 0.44–1.79 0.11–1.68
3.2. Second season practices of PF (2009)
3.2.1. PF field scouting
At the end of growing season 2008, after a field survey which helped in getting vital
information, field scouting depending on interpretation of Landsat ETM+ satellite images,
was done as regular examination of the study area to accurately identify yield-limiting factors
during the growing season, a field scouting record form was prepared as shown in Table 3. It
was necessary to design this field scouting record form to assist in determining all production
inputs and evaluating the current and potential stages of crop production. Investigating the
abovementioned form, it was noticed that, some practices like selecting maize type, seeding
rates, seeding date, some tillage practices and water quality were accepted and followed in
the successive season 2009. On the other hand average of soil fertility status, water quantity,
texture and drainage management, fertilizers application rate and obtained yield were not
convinced and there was an urgent need to develop productivity through the procedures of
precision farming.
Table 3. Field record form (Zea maize single hybrid 10(S.H.10) cultivated 2008).
Zone number
Zone 1. Zone 2 Zone 3 Zone 4
Total area (154.79 acre)
3.87 82.78 62.31 5.83
Soil taxonomy
Typic Torripsamments (USDA, 2006)
Weighted average of soil fertility status mg/kgN 51.05 34.31 17.23 10.97P 14.43 10.84 7.79 3.67K 175.01 179.5 188.4 91.8Fe 8.92 5.46 3.69 2.19Mn 3.04 4.04 2.02 1.61Zn 4.51 3.18 1.38 0.87
Zone number
Zone 1. Zone 2 Zone 3 Zone 4
Cu 0.98 0.91 0.50 0.28O.M.% 1.17 0.74 0.51 0.15pH 1:2.5 extract
7.96 8.06 8.11 8.15
Average salinity dS/m
1.67 1.71 2.14 4.91
Average of CaCO3%
2.03 3.04 3.41 5.50
Tillage
Fall Mulch Tillage. The chisel plow has been the most widely adopted fall mulch tillage tool in El-Salhia area with tandem and offset discs also being used. Disking often resulted in more favorable soil conditions and higher maize yields than chisel plowing.
Variety/Hybrid
Single hybrid 10(S.H.10)
Seeding rate kg/acre
8.57
Seeding date
15 May 2008
Row spacing
0.50 m
Fertilizers rate kg/acre & timing
160 urea (46%) 15 days after planting,60 calcium super phosphate (37.5%) when preparing soil to planting,60 potassium sulfate (48%) 21 days after planting.
Manure application
20 m3 compost/acre
Pivot irrigation systemWater quantity m3/pivot
405,696
Water Salinity (mg/l)
450
YieldHarvest date
16 August 2008
Zone number
Zone 1. Zone 2 Zone 3 Zone 4
Moisture 30%Grain weight tonnes/acre
2.200 1.900 1.600 0.800
NotesSoil texture is sandy except for some subsoil patches of shale. Drainage system not available.
Full-size table
3.2.2. Grid soil sampling
Grid soil sampling provides an initial base of information for developing variable rate
applications plans. This technique uses a systematic method to reveal fertility patterns and
hard pans. After the maize harvest 2008, soil samples were collected in systematic grid (32
in zone 2, 32 in zone 3, 2 in zone 1 and 2 in zone 4) providing location information that
allowed the data to be mapped as shown in Fig. 3. Grid soil sampling aimed mainly at
identifying the current status of nutrients and producing maps of potential nutrient
requirements.
Full-size image (70K)
Fig. 3.
Grid system and soil sampling locations.
3.2.3. Soil mapping based on VRT
Soil mapping based on VRT allowed the project producers to make decisions based on the
detailed maps and knowledge of the studied pivot irrigation field in advance. It gave them
precise control over how much of a given input is applied to specific areas. However, it
involved collecting and processing certain amounts of data, greater amounts of data that
were collected over longer periods of time created more accurate maps. Application maps of
soil nutrients and soil salinity are shown in Fig. 4a–i. These maps which considered as
‘ground-truth’ were produced through GIS system (geostatistical analysis- Kriging
interpolation technique) to give specific details of required inputs for defined management
zone map. In recent studies, soil mapping based on VRT could serve as an effective and
easy-to-use tool for those who practice variable rate applications within-field variability
([Fleming et al., 2000], [ Begue et al., 2008] and[Zhang et al., 2009]).
Full-size image (211K)
Fig. 4.
Spatial variability of soil characteristics.
3.2.4. Variable rate technology/application (VRT/VRA)
In the current work variable rate technology was applied on fertilizers application, irrigation
schedule based on water consumption use and drainage system based on soil salinity and/or
soil compaction. Differential GPS attached with laptop was used to locate the zones
boundary and initial soil samples based upon grid system telling the tractor where specific
locations (site specific) within the field were. The GIS system (geostatistical analysis module)
used this positional information from the GPS to access data about the field at specific
location. Information then was sent to the operator about the field conditions. Using
predetermined calculations, the operator then allowed the required amount of fertilizers to be
distributed, executed the irrigation scheme and identified the shale patches to deal with
throughout the different field zones. VRA could be discussed in the following lines:
3.2.4.1. VRA of fertilizers
In the past years (under TF) fertilizer applications were added haphazardly to the pivot
irrigation field as a whole with same amounts. In PF, project producers wanted to optimize
fertilizers (input) due to the real requirement of specific zones, so zone map based VRT (Fig.
4a–g) were produced to decide the varying amounts of inputs and locations of zones that
require management practices. In the current work, on the basis of precision farming, the use
of fertilizers was limited to areas of known deficiency, and only the deficient nutrient was
applied. Thus, four different application rates across the field were recognized coinciding with
management zones. Fertilizer applications were governed by the yield potential of individual
zone.
Regarding the data obtained from the field scouting form, it was noticed that, applying the
same amount of fertilizers to the field as a whole (TF) led to excessive nutrients especially at
zones 1, 2 and 3, while applications were unsatisfactory at zone 4. Applying excessive
fertilizers in TF led to environmental hazards and economic stress. In PF, fertilizer
recommendations were widely different from one zone to another due to precisely
requirements, where recommendations for nitrogen were 32.4, 93.6, 154.8 and 176.4 kg
N/acre for zones 1, 2, 3, and 4 respectively. The recommendations for phosphorous were
25.2, 40.5, 54, and 72 kg P/acre for zones 1, 2, 3, and 4 successively, meanwhile 59.5, 57.6,
52.2 and 92.6 kg K/acre were added to zones 1, 2, 3, and 4 respectively. [Table 4], [Table
5] and [Table 6] illustrate the variation in NPK recommendations, associated with their costs,
for the pivot irrigation field. [Fig. 5], [Fig. 6] and [Fig. 7] show the NPK applications precision
and traditional farming.
Table 4. Amounts and costs of nitrogen application under PF.
Zone no.Zone area (acre)
Available N in soil
Required N (kg/acre)
Price L.E./acre
Price L.E./zone
mg/kg
kg/acre
Unit
Fertilizer (urea)
1 3.87 51.05 91.89 32.4 70.43 119.74 463.392 82.78 34.31 61.76 93.6 203.48 345.91 28634.43
3 62.31 17.23 31.01154.8
336.52 572.09 35646.93
4 5.83 10.97 19.75176.4
383.48 651.91 3800.64
Total 154.79113.56
204.41457.2
993.91 1689.65 68545.39
Notes: The data represents the upper 0.30 m. of the soil profile. Nitrogen in kg/acre = soil
depth (0.3 m) × area × soil bulk density × nitrogen%. Soil bulk density = 1500 kg/m3. 1 kg
urea cost 1.7 L.E. Traditional application 160 kg N/acre i.e. 348 kg urea (costs 591
L.E./acre = 91573.76 L.E. for the pivot irrigation field). Total costs with PF application
68545.39 L.E. (Egyptian pound).
Table 5. Amounts and costs of phosphorus application under PF.
Zone no.Zone area (acre)
Available P in soil
Required P (kg/acre)
Price L.E./acre
Price L.E./zone
mg/kg
kg/acre
Unit
Fertilizer (Super phosphate)
1 3.87 14.43 25.97 25.2 168 201.60 780.192 82.78 10.84 19.51 40.5 270 324.00 26820.723 62.31 7.79 14.02 54 360 432.00 26917.924 5.83 3.67 6.61 72 480 576.00 3358.08
Total 154.79 36.73 66.11191.7
1278 1533.60 57876.91
Notes: The data represents the upper 0.30 m. of the soil profile. Phosphorus in
kg/acre = soil depth (0.3 m) × area (1 acre) × soil bulk density × phosphorus %. Soil bulk
density = 1500 kg/m3. 1 kg super phosphate cost 1.2 L.E. Total price = price
L.E./acre × zone area (acre). Traditional application 60 kg P/acre i.e. 400 kg super
phosphate (costs 480 L.E./acre = 74299.2 L.E. for the pivot irrigation field). Total costs with
PF application 57876.91 L.E.
Table 6. Amounts and costs of potassium application under PF.
Zone no.Zone area (acre)
Available K in soil
Required K (kg/acre)
Price L.E./acre
Price L.E./zone
mg/kg
kg/acre
Unit
Fertilizer (potassium sulphate)
1 3.87 175.0 315.02 59.5 123.96 557.81 2158.722 82.78 179.5 323.10 57.6 120.00 540.00 44701.203 62.31 188.4 339.10 52.2 108.75 489.38 30493.274 5.83 91.8 165.24 92.7 193.13 869.06 5066.62
Total 154.79 634.71142.46
262.0
545.84 2456.25 82419.81
Notes: The data represents the upper 0.30 m. of the soil profile. Potassium in kg/acre = soil
depth (0.3 m) × area (1acre) × soil bulk density × potassium %. Soil bulk
density = 1500 kg/m3. 1 kg potassium sulphate cost 4.5 L.E. Total price = price
L.E./acre × zone area (acre). Traditional application 60 kg P/acre i.e. 125 kg potassium
sulphate (costs 563 L.E./acre = 87069.37 L.E. for the pivot irrigation field). Total costs with
PF application 82419.81L.E.
Full-size image (32K)
Fig. 5.
Nitrogen applications in both precision and traditional farming.
Full-size image (31K)
Fig. 6.
Phosphorus applications in both precision and traditional farming.
Full-size image (31K)
Fig. 7.
Potassium applications in both precision and traditional farming.
In a comparison between TF and PF, under precision farming, nitrogen application was
reduced by 0.494, 5.496, 15.122 tonnes in zones 1, 2 and 3 respectively. On the other hand
zone 4 required an excessive amount of nitrogen determined by 0.096 tonnes. Phosphorous
application was reduced by 0.135, 1.614, 0.374 tonnes in zones 1, 2 and 3 successively. On
the other hand zone 4 required an excessive amount of phosphorous determined by 0.070
tonnes. Finally potassium application was reduced by 0.002, 0.199, 0.486 tonnes in zones 1,
2 and 3. On the other hand zone 4 required an excessive amount of potassium determined
by 0.190 tonnes. From the abovementioned lines, it is concluded that, variable rate
application of fertilizers saved amounts of 21.02, 2.05, 0.50 tonnes N, P and K respectively
for the experimental pivot field (154.79 acre).These results were agreed with what was found
by Lan et al. (2008) where fertilizers under VRA for maize were saved by 29 to 32%, the
yield was significantly increased by 11–33% more than that in the conventional application,
and emphases the VRT of fertilizers on the ecological benefits. Similar results were reported
by Wittry et al. (2004) , and Xue et al. (2004) .
The investigated soils contained sufficient levels of micronutrients to meet crop demands.
3.2.4.2. VRA of compacted saline field spots
Remote sensing and VRT maps with the aid of GPS led the operator to specific areas that
suffering from compaction and/or salinity. Shale deposits (very fine massive clay) associated
with salinity represented serious limiting factors affecting root zone aeration, nutrients uptake
and water movement through and/or downward into the soil (El Nahry, 2007 ). Shale deposits
were found in small patches in the studied area. Before applying fertilizers or defining the
irrigation scheme to the compacted and or/saline spots, vertical drainage was executed as
shown in Fig. 8. In the second season, the treated spots showed a higher plant growth as a
result of aeration and leaching excessive salts compared by the first one. Although this type
of drainage is very simple and too cheap compared by tile drainage, it requires continuous
management to be maintained.
Full-size image (50K)
Fig. 8.
Vertical drainage.
3.2.4.3. VRA of water consumption use
3.2.4.3.1. SEBAL model (based on satellite images)
The surface energy balance algorithm for land (SEBAL) model was used for estimating crop
evapotranspiration (ETc) on the experimental pivot irrigation field scale under local climatic
conditions of Ismailia governorate. SEBAL is an image processing model comprised of
twenty five sub-models for calculating evapotranspiration as a residual of the surface energy
balance. SEBAL is an emerging technology and has the potential to become widely adopted
and used by water resources and irrigation community (Allen et al., 2002).
Applying water in adequate amounts could be considered the cornerstone of variable rate
application especially with shortage of irrigation water in such arid regions. First of all land
surface temperature (LST) was derived (Eq. (1) and (2)) for all 16 days instantaneous
acquired ETM+ images consequently as 31.6, 34.7, 34.2, 34.3, 34.4 and 35.2 for May 28,
June 13, June 29, July 15, July 31and August 16. Using the derived LST and metrological
data of relative humidity, wind speed and sunshine hours, a daily reference
evapotranspiration (ET24) is computed by solving the surface energy balance using
Eq. (3) and (4). Based on the actual cropping calendar, the weighted crop coefficient Kc for
different satellite overpass dates was calculated (Eq. (5)) as 0.38, 0.87, 1.20 and 0.75
successively within the phonological stages initial, development, mid-season and end-
season. Fig. 9 shows that with 28.5 spatial resolution of ETM+ image, ET24 variable rate of
initial stage on May 28 has a value of 4.03 mm. At development stage on June 13,
ET24 values were ranged from 5.1 to 5.8.At mid-season stage on 29 June and July 15, ET24
values were ranged from 5.1 to 6.2 mm. At end-season stage on July 31 and August 16,
ET24 values were ranged from 5.2 to 6.5 mm. Higher values (dark blue color with ET24 value
of 6.5 mm) appear in the centre of the studied pivot; meanwhile, the outer pivot land (yellow
color) shows low ET24 values of 3.15 to 4.03 mm. Missing values of ET24 were obtained by
daily calculation of reference evapotranspiration (ETo) using the modified Penman–Monteith
method.
Full-size image (108K)
Fig. 9.
Different date's instantaneous evapotranspiration of maize mm/day.
Solving the Eq. (6) the ET24 was converted into potential crop evapotranspiration (ETc)
recording 1.53, 4.87, 5.31, 7.38, 7.56 and 8.53 mm/day. Spatial patterns for various classes
that include the outer pivot land and agricultural crops in the experimental pivot irrigation field
and adjacent pivots were shown in Fig. 9 as well.
Results obtained from using SEBAL with the aid of Penman–Monteith method throughout
Cropwat model indicated that, accumulated water consumption use of the investigated pivot,
averaged 483 mm for maize grown without water deficit. So it is worthy to say, under PF the
total quantities of irrigation water that added to maize growing in the investigated field
(154.79 acre) were determined at 311,978 m3/growing season against 405,696 m3/growing
season under TF, saving an amount of water equal to 93,718 m3. The obtained results were
in an agreement with results obtained by Al- Kufaishi et al. (2006) who found loss of irrigation
water was higher for the uniform application than that for the variable rate application (VRA).
To demonstrate crop water requirements and irrigation scheme of maize, FAO Cropwat
model was used.
3.2.4.3.2. Cropwat model (based on FAO Penman–Monteith approach)
This approach overcomes shortcomings of the previous FAO Penman method. From the
original Penman–Monteith equation and the equations of the aerodynamic and surface
resistance, the FAO Penman–Monteith model was used to estimate ETo as follows:
- Soil moistureFrom field measurements that were matched with Cropwat results, the total available soil moisture (field capacity – wilting point) was determined at 58.9 mm/meter, maximum rooting depth was determined at 100 cm. Initial available soil moisture was determined at 58.9 mm/meter. There is no initial soil moisture depletion.- Reference evapotranspiration (ETo)The FAO Penman–Monteith method through Cropwat window model was recommended as the sole method for determining ETo. This method explicitly incorporates both physiological and aerodynamic parameters. To determine ETo in the experimental pivot field, daily climatic data within the growing season were used, meanwhile for simplicity, monthly climatic data and associated ETo was displayed in
Fig. 10. Monthly ETo values were determined as 5.84, 6.58, 6.28 and 5.78 mm for the growing season (May, June, July and August 2009 successively). The reference crop evapotranspiration was at peak (5.84 mm/day) at the initial stage; slightly increased at development stage (6.58 mm/day) than at mid season stage (6.28 mm/day), meanwhile it decreases at end-season stage to reach 5.78 mm/day. Decreasing of ETo values may be due to increasing of relative humidity.
Full-size image (20K)
Fig. 10.
Monthly climatic data and associated ETo for maize.
3.2.4.3.3. Crop water requirement (CWR)
The amount of water required to compensate the evapotranspiration loss from the cropped
field is defined as crop water requirement. Although the values for crop evapotranspiration
under standard conditions (ETc) and crop water requirement are identical, crop water
requirement refers to the amount of water that needs to be supplied, while crop
evapotranspiration refers to the amount of water that is lost through evapotranspiration. Crop
water requirement were illustrated in Table 7 and Fig. 11 as well.
Table 7. Total ETc and irrigation requirements during the growing season of maize.
Month
Decade
Stage
Kccoeff
ETc(mm/day)
ETc(mm/dec)
Eff. rain (mm/dec)
Irrigation required (mm/dec)
May 2 Init 0.3 1.75 10.5 0 10.5May 3 Dev. 0.31 1.90 20.9 0 20.9June 1 Dev. 0.64 4.08 40.8 0 40.8June 2 Mid 1.07 7.17 71.7 0 71.7June 3 Mid 1.2 7.84 78.4 0 78.4July 1 Mid 1.2 7.64 76.4 0 76.4July 2 Late 1.13 7.08 70.8 0 70.8July 3 Late 0.87 5.34 58.7 0 58.7August 1 Late 0.61 3.64 36.4 0 36.4August 2 Late 0.41 2.38 14.3 0 14.3
478.9 0 478.9
Full-size image (68K)
Fig. 11.
Crop water requirements.
As shown in Table 7 and Fig. 11, the initial stage required 10.5 mm/decade of water due to
plant limited growth and somewhat climatic conditions. By the end of development stage
irrigation water requirements increased to reach 40.8 mm/decade. The highest amount of
required irrigation of 78.4 mm/decade was recorded at the third decade of June (mid-season
stage) due to the higher plant growth. Needs to water was reduced sharply in the third
decade of July (late-season stage) till it reaches the lowest value of 14.3 mm in the second
decade of August to encourage the grains maturity. Generally ETc ranged between 1.75 and
7.84 mm/day during the growing season, while total ETc was determined at
478.9 mm/growing season. Reviewing the obtained results from the two used models, it was
noticed that, insignificant difference was found between ETc determined by SEBAL (483 mm)
and that determined by Cropwat (478.9 mm) .So it is worthy to say using remote sensing
with tighten temporal resolution (quick site revisit i.e. daily visit) and GIS to determine ETc is
essential, especially in areas that not covered by meteorological stations like Sahara.
3.2.4.3.4. Irrigation schedule of maize
Essentially, maize irrigation schedule included calculations, producing a soil water balance
on a daily step. Irrigation schedule always rely on gross and net irrigation. Gross irrigation
(GI) represents the water depth in mm applied to the field while net irrigation (NI) represents
the water depth in mm that is used beneficially. This allowed developing indicative irrigation
schedules to improve water management. Table 8 shows that both gross and net irrigation
were increased at the initial stage from 23.2 and 16.2 to 56 and 39.2 mm respectively on the
46th day of mid-season stage, then they decreased to reach 51.4 and 36.2 mm successively
on the 61st day of the end-season stage. On the 86th day of the end-season, both of GI and
NI increased again reaching their maximums of 64.5 and 45.1 mm. More water supplies at
end-season stage encouraged grain weight/size. Water depletion increased gradually from
initial stage to end –season stage to reach its maximum of 75% one day before harvest.
Actual evapotranspiration (ETa) recorded 100% at all growing stages because water stress
coefficient (Ks) was equal to 1.0 so, ETc adj value equaled ETc. Converting the Gross
irrigation application depth into a permanent supply was called “flow” which was estimated by
12.64 l/s/he. A remarkable difference between gross irrigation (653.4 mm) and net irrigation
(457.4 mm) led to irrigation efficiency of 70%.
Table 8. Irrigation schedule (Gross and net irrigation).
DateDay
Stage
Ks fract.
ETa
%Depletion %
Gross irrigation (mm)
Net irrigation (mm)
Flow (l/s/ha)
23-May 9 Init 1 100 56 23.2 16.2 0.32-June 19 Dev 1 100 57 33.4 23.4 0.399-June 26 Dev 1 100 58 40.8 28.5 0.6714-June 31 Dev 1 100 59 46.8 32.7 1.0819-June 36 Mid 1 100 60 51.2 35.8 1.1824-June 41 Mid 1 100 64 55.1 38.5 1.2729-June 46 Mid 1 100 65 56.0 39.2 1.34-July 51 Mid 1 100 64 54.7 38.4 1.279-July 56 Mid 1 100 64 54.6 38.2 1.2614-July 61 End 1 100 60 51.4 36.2 1.1920-July 67 End 1 100 71 60.7 42.5 1.1728-July 75 End 1 100 71 61.0 42.7 0.888-August 86 End 1 100 75 64.5 45.1 0.6816-August
End End 1 0 32
Total 653.4 457.4 12.64
3.2.4.3.5. Soil water retention
Fig. 12 shows the relationship between soil water retention and days after planting
considering readily available moisture (RAM), total available moisture (TAM) and water
depletion. It was noticed that at initial stage within 10 after planting there was a narrow gab
between RAM and TAM with low water depletion, where soil water retention fall in the range
of 10–30 mm,this gab was enlarged to reach its maximum after 35 days from planting (end of
development-season stage),then it was stabilized for 25 days at mid-season stage realizing
soil water retention in the range of 32.5 and 60 mm and finally at the end-season stage this
gab between RAM and TAM was narrowed again to reach soil water retention in the range of
47.5 and 60.0 mm. Water depletion was increasing with time.
Full-size image (83K)
Fig. 12.
Relationship between soil water retention and days after planting.
3.2.5. Correlation analysis of NDVI vs. maize yield
Remote sensing measures specific wavelengths of light that are reflected from the leaves of
plants in the field. In addition to light in the visible spectra, light in the near infrared spectrum
(NIR, which is not visible with the naked eye and is reflected by the plant) is measured as
well (Martin, 2004). Larger plants with more leaves will reflect more NIR light than smaller
plants, just as healthy vigorous plants of a given size will reflect more NIR light than stressed
plants of the same size. Reflectance data were measured and used to calculate NDVI, which
has been found to be correlated to plant size, vigor and yield of crops. In this study, at
different critical periods the correlation between NDVI and yield was derived. Though
variations could be observed between NDVI and yield, yet a positive correlation was
obtained representing a linear relation,where lower range of NDVI that represented by 0.04–
0.17 reflects the lower yield of 1.3 tonnes/acre, meanwhile the higher range of NDVI (0.43–
0.57) reflects the higher yield of 3.0 tonnes/acre,[Fig. 13] and [Fig. 14].
Full-size image (45K)
Fig. 13.
NDVI under PF.
Full-size image (50K)
Fig. 14.
Yield map under PF.
Regression analyses showed a positive relationship between NDVI and grain yield (Fig. 15),
where regression equation was represented by: y = 3.9217x + 1.0071 with R2 up to 0.9743,
where x = NDVI.
Full-size image (24K)
Fig. 15.
Relationship between NDVI and grain yield.
3.2.6. Change detection of management zones and yield
Factors limiting the productivity of a field often arise suddenly and must be corrected quickly
to preserve the full yield potential of the crop (OMAFRA Staff, 2008) The obtained data were
transferred to a yield map using ARC MAP software (Fig. 11). It was noticed that, there was
a dramatically change in areas of management zone between TF&PF where under PF at
least three zones were merged to represent 84.3% of the pivot irrigation field area. On the
other hand, yield under TF recorded 2200, 1900, 1600 and 800 kg/acre meanwhile it
recorded 2300, 3000, 2400 and 2000 kg/acre under PF achieving a remarkable yield
increase of 1000, 2100, 800 and 200 kg/acre in the management zones 1, 2, 3 and 4
respectively.
3.3. Precision farming profitabilityPrecision farming profitability could be discussed through two approaches: 1, economic
profitability and 2, environmental profitability.
3.3.1. Economic profitability
Lambert and Lowenberg-DeBoer (2000) reviewed 108 studies regarding precision farming
profitability, reporting that 63% of the studies indicated positive net returns for a given PF
technology, while 11% indicated negative returns. There were 27 articles indicating mixed
results (26%).
The economic profitability of precision farming is as variable as the field conditions. In highly
uniform fields, better knowledge of soil and plant parameters is not as likely to result in
greater economic return as it is in fields with variable conditions. In the experimental pivot
return and costs were compared in both TF and PF as illustrated in Table 9. Although costs
of applying PF were much higher than TF, total returns increased from 472,773.0 at TF to
784,675.5 LE at PF (65.97%) at PF. The economic profitability (returns-costs) recorded
238,298.42 LE for TF and 284,133.39 LE for PF representing an increase of (29.89%).
Table 9. Comparison between returns/costs in both TF and PF.
Traditional farming (TF 2008)
Precision farming (PF 2009)
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
Returns (L.E.)Yield/tones 2.200 1.9001.6000.8002.300 3.0002.4002.000Price (LE/tonnes) 1750 1750 1750 1750 1750 1750 1750 1750
Acreage (acre) 3.87 82.7862.315.83 0.80130.49
20.193.31
Total returns (L.E.) 472773784675.5
Costs (L.E.)Grid sampling – 2500Chemical analyses – 6800Remote sensing – 21000Computer(Laptop) – 5000
Traditional farming (TF 2008)
Precision farming (PF 2009)
Zone 1
Zone 2
Zone 3
Zone 4
Zone 1
Zone 2
Zone 3
Zone 4
Software's – 50000GPS – 5000Equipped tractor – 200000
Chemical fertilizers252942.33
208842.11
Total costs (L.E.)252942.33
499142.11
Profit (total returns − total costs)
219830.67
285533.39
Note: LE = Egyptian pound.
3.3.2. Environmental profitability
Excessive fertilizers and inadequate quantities of irrigation water applied to plants during
crop maintenance may leach into the ground water through deep percolation causing
pollution. Nutrient pollution has consistently ranked as one of the top causes of degradation
in waters for more than a decade. Excess nutrients lead to significant water quality problems
including harmful algal blooms, hypoxia and declines in wildlife and wildlife habitat, especially
in Egypt many people still drinking the ground water. From the environmental point of view
under PF macro nutrients application decreased by 23.566 tonnes and irrigation water
decreased by 93,718 m3/experimental pivot area. This decrease in fertilizers use and
irrigation water limited the environmental hazards especially pollution caused by excessive
nitrate.
4. ConclusionsThe study aimed to improve land and water use efficiency and to determine the profitability of
precision farming practices environmentally and economically against traditional farming
practices. The conclusions drawn from this study indicate that, precision agriculture offers
the potential to automate and simplify the collection and analysis of information. It allows
management decisions to be made and quickly implemented on management zones within
the fields. Maize was receptive crop to PF when controlling the variable input of fertilizers,
water consumption and management practices. NDVI has been found to be correlated
significantly to the yield of crops as at different critical periods the correlation between NDVI
and yield was highly significant. NDVI could express the biomass so far and also could
define the management zone perfectly. Soils with much variability seemed to be more
responsive to management practices, i.e. optimum fertilizers input in each zone, water
consumption and vertical drainage in the scattered spots of subsurface shale layers.
Economic and environmental profitability of PF has been achieved by potentially reducing
input costs, increasing yields, and reducing environmental impacts (excessive water and
fertilizers) through better matching inputs applied to crop needs. Remote sensing has proved
that it is a promising tool for determining water consumption use especially in those areas
that are not covered by meteorological stations i.e. Sahara.
Finally it is worthy to say PF is essential economically by improving revenues or cash flow
and environmentally through reducing input losses and increasing nutrient uptake efficiency.
RecommendationsThis study recommends that, the database about how to treat different areas in a field
requires years of observation and implementation through trial-and-error to get the best
results to be adopted by the land users. Today, that level of knowledge of field conditions is
difficult to maintain because of the larger farm sizes and changes in areas farmed due to
annual shifts in leasing arrangements. However, we would expect that PF would be more
feasible with producers who could either spread the technology costs over a large number of
acres or could control production practices for more than one input. Finally it is worth to say
adopting advanced technologies of precision farming using remote sensing and GIS
techniques is the key issue for maximizing the output of the farm production system.
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