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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