anjali DS 2 (precision farming).pdf

Maximize production in Agriculture

2. Speaker:
Anjali Patel
PhD (Agronomy)
INDIRA GANDHI KRISHI VISHWAVIDYALAYA, RAIPUR
Submitted to:
Dr. N.K. Choubey
Professor & Course In-charge
Dr. N. Pandey,
Professor and Co-I/C
Course No.- AGRON- 692

3. •Precision - it may be defined as the degree of refinement
with which an operation is performed or a measurement
stated.
•Farming - Farming is the act or process of working the
ground, planting seeds, and growing edible plants or raising
animals for milk or meat.

4. •Definitions of precision farming
•History
•Needs
•Concept
•Objectives
•Prospects
•Components
•Applications
•Advantages
•Limitations
•Summery
•References.

5. •“Precision agriculture can be defined as the application of
principles and technologies to manage spatial and temporal
variability associated with all aspects of agricultural production
for the purpose of improving crop performance and
environmental quality.”
(Pierce and Nowak, 1999)
•“Precision farming is the only solution to identify the causes of
variability within the field and to carefully tailor soil and crop
management to fit in each cultivated field.”
(Gautam and Sharma, 2002)

6. •In the 1960s and 1970 the Geographic Information System
(GIS) was one of the first precision farming tools.
•During mid 1970’s Pushparajah coined the term “discriminatory
fertilizer use”.
•In late 1980s the tool to tie all variabilities together was the
Global Positioning System (GPS).
•The term precision agriculture appears to have been used first
in 1990.
•Pierre Robert- father of PF.
•The present status of precision agriculture is similar to no tillage
concept of 1960.

7. •Increased land degradation.
•Depletion of water resources.
•For maximum use of minimum land unit.
•Socio economic need.
•Environment pollution.
•For increasing the effectiveness of inputs.

8. •60 to 80 % higher yield in all the crops
(The highest possible yield records under Indian conditions)
•90% plus first grade marketable produce
•30% premium price in the market
•5-6 days more shelf life
•Less labour dependence
•30-40 % Water economy
•Empowerment of farmers technically, economically and socially

source – Tamil Nadu Precision Farming Project, 2007

9. S.No. Traditional Farming Precision Farming
1. Whole field approach where field is
treated as a homogeneous area
Farm field is broken into “management
zones”
2. Decisions are based on field averages Management decisions are based on
requirement of each zone
3. Inputs are supplied uniformly across
the field
PF tools (e.g. GPS/GIS) are used to control
zone
4. Low yield with high inputs High yield with low inputs

12. Concept is simple……
•Right input
•At right time
•In right amount
•At right place
•In right manner


•Assessing variability
•Understanding variability
•Managing variability

13. Replace
•Big machinery
•High energy consumption
• Over application of chemicals
With
•Intelligent machines
• Intelligent processes

14. •To enhance the productivity in agriculture.
•Prevents soil degradation in cultivable land.
•Reduction of chemical use in crop production.
•Efficient use of water resources.
•Dissemination of modern farm practices to improve
quality, quantity and reduced cost of production in
agricultural crops.

15. •Agronomic perspective
•Technical perspective
•Environmental perspective
•Economical perspective

17. •GPS is a satellite based signal
broadcast system that allow
GPS receivers to determine
their position.
•It helps users to record
positional information
(latitude, longitude and
elevation) which is useful in
locating the spatial variability
with accuracy.


P
or
ta
bl
e
G
P
S

18. •GIS is a computer based system or a type of
computerized map, provides information on
field variability.
•It is the brain of precision farming system.
•Components of GIS are:
Hardware
Spatial data
Software
Procedures
Expertise
•Database will contain layers of spatial data
from remote sensors, existing maps or field
surveys.

19. •Collects data from reflected
electromagnetic energy and converts
it into images using satellites or
airplanes.
•The specific remote sensing
techniques can be used for-
Detection
Identification
Measurement
Monitoring of agriculture
phenomena.
•Satellites used in remote sensing- RRS-
IA, RRS-IIB, RRS-IIIC, IRS-P6.

22. •Refers to any equipment
designed to allow the rate of
farm inputs to be precisely
controlled and varied while
the machine is in operation.
•These are automatic and
may be applied to numerous
farming operations.

24. •Precision agriculture technologies such as variable-rate fertilizer
applicators can increase cotton profitability by improving
nutrient use efficiency.

Source: Phillips et al.(2008) Better Crops, 92 ( No. 3)

25. •Yield monitors are
attached to conveyors or
combines to measure grain
yield and moisture content.
•Identifies in-field
variations in yield.

26. Farming cannot be imagined without farmer
For assessing and managing the variability, decision-making
is the key factor, and it is to be done in consultation with
the farmer.

27. Precision Agriculture Cycle

28. •Information / Data base
•Technology
•Management

31. •Site Specific Nutrient Management (SSNM) - Leaf Color Chart
(LCC) and Chlorophyll meter (SPAD)

•Integrated Nutrient Management

•Application of organics (FYM/Bio Compost/Cakes/Green
manuring/Crop residues)

33. •The leaf color chart
(LCC) is an easy-to-use
and inexpensive
diagnostic tool for
monitoring the relative
greenness of a rice
leaf as an indicator of
the plant N status.

A standardized leaf color chart for
assessing leaf N status

34. N deficiency
Apply high N dose
Immediately
Still showing N deficiency
Apply less N dose
very soon
Less N deficiency
Apply baseline N dose


Surplus of N
Do not apply N

35. •Released in 1984 (Minolta
Co. ltd., Japan).
•Most widely used chlorophyll
meter is the hand-held
Minolta SPAD-502.
N = [6+ (7 × D)] ×1.14
•N represents fertiliser-N (kg
ha
-1
) needed for optimal
growth and D is the
difference between average
SPAD meter readings from
the test field and the over
fertilized reference plot.

36. •Based on NDVI that is correlated
with leaf chlorophyll, side dress
nitrogen rates that are aligned
with site specific crop needs can
be prescribed.
•NDVI =
?????????????????? − ??????��
??????????????????+??????��

•NDVI can range from 0.00 to 0.99

NDVI - normalized difference
vegetation index
NIR – Near Infrared
High NDVI = low N requirement
Less NDVI = high N requirement

37. Figure. Differences in reflected light between a healthy and unhealthy leaf.
(Source: Brenda Ortiz, 2011)

39. •Nutrient Expert® and Crop Manager are examples of
decision-support systems developed for SSNM in
cereal production systems.

40. •Nutrient Expert® is an
interactive, computer-
based decision-support
tool that enables
smallholder farmers to
rapidly implement SSNM
in their individual fields
with or without soil test
data.

41. •Crop Manager is a computer
and mobile phone based
application that provides small-
scale rice, rice-wheat, and
maize farmers with site- and
season-specific
recommendations for fertilizer
application.
•The software is freely
downloadable at
http://cropmanager.irri.org/ho
me.

42. •In the West Bengal, Islam et al. (2007) observed a saving up to
31.4 kg N ha
-1
while following precision N management with
LCC.
•Thind et al. (2010) followed LCC shade 4 as threshold leaf
greenness for applying need-based fertilier-N to rice and
reported fertiliser-N saving along with significantly higher grain
yields than with blanket fertiliser recommendation at Ludhiana.
Singh et al., (2014)

43. •Hussain et al. (2003) found the critical SPAD value of 37.5
appropriate for guiding need based N top-dressing in rice in
Pakistan.
•In Bangladesh, Kyaw et al. (2003) obtained significantly higher
yields with 3 to 12% less fertiliser-N use in comparison to the
blanket recommendation by using SPAD value 35 as the
threshold SPAD value.
Singh et al., (2014)

44. Site, Crop and
Sensor
Salient Findings Reference
North western
India, direct
seeded rice,
GreenSeeker
Nitrogen recovery eficiency increased by
more than 12% by applying sensor-
guided fertilizer N dose as compared to
when fertilizer N was managed as per
standard recommendation
Ali et al.,
(2015)
Hebei Province
(China),
wheat and maize,
GreenSeeker
GreenSeeker-based precision N
management strategy was consistently
better for both wheat and maize in terms
of reduced fertilizer N application and
higher fertilizer N use eciency than
observed with farmer’s practice and
regional optimum N management.
Cao et al.
(2017)
Singh and Ali, 2020

45. •Banerjee et al. (2014) conducted an experiment on precision
nutrient management in maize using NE as decision support
system. It was found that NE recommendation gave highest
yield, agronomic efficiency (52.51% and 84.01%),
physiological efficiency (30.04% and 44.56%) and recovery
efficiency (17.28% and 27.17%) over state recommendation
and farmers’ practice.
•Nutrient Expert-based nutrient management in maize produced
14.7% higher yield over soil test based recommended dose
and improved economic benefit by Rs 7,856 ha
-1
(Kumar et al.,
2015).
Singh et al., (2014)

46. 4.

47. Crop Average
yield
target
(t/ha)
Average grain yield (t/ha) CD@ 5%

SSNM RDF FFP
Maize 7 7.02 5.98 5.44 0.48
Rice 9 8.34 7.47 6.74 0.63
Wheat 3.75 3.79 3.22 2.85 0.28
Rabi jowar 2.75 2.56 2.09 1.89 0.18
Sunflower 2.75 2.44 2.01 1.8 0.15
Chickpea 2.75 2.39 1.99 1.89 0.1
Cotton* 2.75 2.55 2.21 2.01 0.17
Chilli** 2.25 2.18 1.94 1.76 0.16
*seed cotton yield, **dry chilli yield
Source:Biradar et al. (2012)

48. Treatment Plant
height
(cm) at 60
DAS
Dry matter at
harvest (gm
/plant)
SPAD
Chlorophyll
Meter Reading
at 60 DAS
Grain
yield
(t/ha)

Stover
yield
(t/ha)

Nutrient management
Control 166.5 70.30 15.8 1.14 4.29
State Recommendation 180.05 79.12 24.6 3.52 5.69
Farmer’s practice 175.6 79.20 19.4 2.67 4.63
Nutrient expert® 180.4 81.75 26.2 4.64 6.59
LCC based application 180.5 76.31 29.8 4.47 6.49
SEm (±) 4.3 3.1 2.3 0.42 0.53
CD (p=0.05) 11.8 9.3 7.4 1.3 1.6
Variety
Sona 173.08 76.5 24.9 4.16 6.24
Rajkumar 180.08 79.3 27.5 3.39 6.38
SEm (±) 3.6 2.7 3.33 0.7 0.72
CD (p=0.05) 10.4 (NS) 9.0 (NS) 10.6 (NS) 2.3 (NS) 2.9 (NS)












source: Banerjee et al. (2014); West Bengal

49. Treatment Agronomic
efficiency (AE)

Recovery efficiency
(RE)

Physiological
efficiency (PE)
Nutrient management
Control
State Recommendation 15.86 37.07 42.79
Farmer’s practice 19.12 41.21 46.40
Nutrient expert® 29.16 53.59 54.42
LCC based application 26.64 49.98 53.30
SEm (±) 0.40 0.96 0.74
CD (p=0.05) 1.3 2.4 2.1
source: Banerjee et al. (2014); West Bengal

50. Crop Input/
Factor
Region Methodology Results of using VRT
Thrika
wala
et al.
(1999)
Corn
N Ontari
o,
Canad
a
Simulation model
(Barry et al., 1993)
to estimate N
leaching.
NO
3–N leaching reduced
by 13%, average or
between 4.2% and 36.3%
in high and low fertility
areas, respectively.
Bonha
m and
Bosch
(2001)
Corn
P Virgini
a
Used chemical
loading information
from Virginia
Department of
Conservation and
estimated P leaching
with linear
programming.
Use of site-specific
information allows for
more accurate predictions
of P pollution potential.
Bongiovanni and Deboer, 2014

51. Micro irrigation –
Drip/sprinkler
method

Fertigation

Through laser aided
land leveler
Agriculture contributes less than
25 % to India’s GDP
whereas it consumes 78 % of
India’s water resources

52. •Saving of water by 30 to
50% as compared to
conventional mode of
irrigation.
•Yield increase from 50 to
100%.
•Nutrients can also be
supplied to the plant
through the drip system,
which is called Fertigation.

53. •Suitable for all open field
close spaced crops.

•Suitable for a variety of crops
such as coffee, tea etc.

•Discharge rate- 1000 l/h
•Pressure of nozel- 2.5 bar
•Distance- 10 m.

54. •These are the best tools for
under-foliage irrigation for
many crops like citrus, apple,
banana etc.

•Good for irrigating close
growing vegetable crops.

•Discharge rate- 28-223 l/h
•Pressure of nozel- 0.8-4 bar
•Distance- 0.9-4 m.

57. Water use
efficiency
Dukes, 2004 It has been reported that precision
irrigation (Drip and Sprinkler) can
improve application efficiency of water
up to the tune of 80-90% as against
40-45% in surface irrigation method.
Water
savings
Hedley and
Yule (2009)
suggested water savings of around
25% are possible through improvements
in application efficiency obtained by
spatially varied irrigation applications.
Yield and
profit
by King et al.
(2006)
carried out an experiment for
measuring the yield of potatoes under
spatially varied irrigation applications.
It was reported that yields were better
in two consecutive years over uniform
irrigation management.

58. Particulars Drip villages Control villages
Quantity of water applied (M
3
) 8979* 12669
Quantity of energy consumed (kWh) 2219* 8294
Cost of labour (Rs) 9761* 31487
Yield (tonnes) 60.34* 57.79
Gross income (Rs) 280602* 267400
Yield per unit of water (kg/M
3
) 7.4* 4.9
Yield per unit of energy (kg/kWh) 28.6* 7.2
Returns per unit of water (Rs/M
3
) 23.8* 13.3
Returns per unit of energy (Rs/kWh) 92.3* 19.8
Source: Field survey during 2007-08
*indicates that values are significantly different.
Kumar and Palanisami, 2010

59. Treatments Crop yield
t ha
-1
Number
of fruits
per plant
Fruit
weight
g
Fruit
thickness
cm
Conventional
Irrigation
22.47 a 3.90 a 1213 a 3.40 a

Drip Irrigation 24.54 ab 4.63 a 1300 b 3.70 ab
Drip Irrigation
+ Plastic Mulch
27.07 b 4.77 a 1383 c 4.10 b


Means in the same column with different letters differ significantly
at 0.01probability level
Seyfi and Rashidi, 2007; Iran

60. Treatments Water applied
cm

Water use efficiency
t ha-1 cm-1
Conventional Irrigation 39.1 0.57
Drip Irrigation 33.9 0.72
Drip Irrigation + Plastic Mulch 29.9 0.91
Seyfi and Rashidi, 2007; Iran

61. Treatment Broccoli Cauliflower
Yield
(t/ha)
WUE
(kg/ha-cm)
Yield
(t/ha)
WUE
(kg/ha-mm)
DINM
100 11.6b 103.3 21.6b 200.1
DINM
80 12.5b 138.3 20.4b 237.7
DIM
100 12.9b 115.4 23.0a 212.7
DIM
80 13.3a 148.1 21.3a 247.6
FINM
100 8.9c 46.7 18.6c 101.0
FIM
100 9.9c 52.5 19.6c 106.6
Means separated by different subscripts are significantly different at P<0.05
Patle et al., 2018; Sikkim, India

62. Treatments T. pod yield (kg/fed.) T.Kernel yield (kg/fed.)
I
3 1625.40a 1105.25a
I
5 1409.66b 954.00b
Significance L. *** **
M
1620.60a 1142.12a
M
0 1414.46b 918.00b
Significance L. *** ***
I
3 M 1658.80 1176.00
M
0 1592.00 1036.50
I
5 M
1582.41 1108.26
M
0 1236.91 802.50
Significance L. *** ***
Means within each column followed by the same letter/s are insignificant. *significance
at the 0.05, * * significance at the 0.01, * * *significance at the 0.001.
Zayton et al., 2014;

63. Weed detection: Processed image
Red = Johnson grass
Yellow = Spurge
Green = Cotton
Black = Unclassified

65. •Increase in yield and plant
productivity up to 20%.
•Prevents weed growth.
•Maintains soil moisture
leading to reduced need
for irrigation.
•Improved seed
germination.

66. •Herbigation is an
effective method of
applying herbicides
through irrigation
systems.
•It provides greater
flexibility in weed
control programs.

67. Crop Input/
Factor
Region Methodology Results of using VRT
Heisel et
al.
(1996)
Barley
Herbi
cides
Denmark Measured
reduced
chemical
loading.
*Reduction of 66–75%
in herbicide use.
Timmerm
ann et al.
(2001)
Wheat
Barley
Sugar
beet
Corn
Herbi
cides
Bonn,
Germany
Measured
reduced
chemical
loading.
*Reduction of 54% in
herbicide use (˛33 ha
-1
)

*Decrease in
environmental damage
(ground and surface
water with herbicides).
Bongiovanni and Deboer, 2014

68. Treatments Mean kapas yield
(kg/ha)
Wet weed wt./plot
(gm) at 45th day

Black LLDPE (20
micron)
673 303
Coir pit @ 12.5 t/ha 565 575
Organic mulch @ 12.5
t/ha
509 510
No mulch 436 1121





Source- TNAU,

69. Nalayini et al., 2013; Coimbatore,

70. *Yields followed by the same letter are not significantly different at
the P = 0.05 level
I
1 = ET
p, I2 = 0.8 ET
p and I
3 = 0.6 ET
p and chemigation method C
1,
and traditional method C
o.
Sayed and Bedaiwy , 2011; Egypt

71. •Net houses
• Pests and disease monitoring/detection through
Remote Sensing and GIS

72. •Net houses- plastic nets are
used for protection of crops
against damage from birds,
insects, hails and severity
solar radiation during
summer.

73. Use of GIS and Remote Sensing for insect pest and
disease detection or monitoring so that we are able to
control these infestation precisely and timely.

74. Crop Input/
Factor
Regio
n
Methodology Results of using VRT
Weisz
et al.
(1996)
Potato
Malat
hion
(Insecti
cides)
Penns
ylvani
a
Measured reduced
insecticide
application.
Precision IPM signifi
cantly reduced insecticide
inputs by 30–40%.
Midgar
den et
al.
(1997)
Potato
Malat
hion
(Insecti
cide)
Penns
ylvani
a
Field and
laboratory tests.
Measured pest
density and
insecticide
resistance from
season to season.
Precision IPM significantly
reduced the rate of
development of
insecticide resistance,
conserving natural
enemies.
Bongiovanni and Deboer, 2014

75. Figure- Schematic of the platform comprising neck
mounted collar wirelessly downloading behavioural
data of individual animals to a local PC for
presentation and decision support applications.
Andonovic et al., Precision Livestock Farming Technologies

76. Reduction in
cost of
cultivation
Baird et al.
(2001)
Lowered rate of 1,3-Dichloropropane
(Nematicide) and obtained environmental
benefit when site specifically applied
nematicide to control Meloidogyne
incognita on cotton at Georgia.
Dammer et al.
(2003)
Recorded an average herbicide saving of
24% and fungicide saving of 19% due to
variable rate application of plant
protection chemicals.
Increase in
input
efficiency
Delegado et
al. (2001)
Recorded increased N use efficiency with
precision farming and stated that NO
3-N
can potentially be removed from shallow
underground water table, thus protecting
environment.
Reduction in
pollution
Munch et al.
(2000)
Site specific application of N reduced
nitrous oxide emissions by 33%.

77. •Space Application Centre, ISRO, in collaboration with Central
Potato Research Institute, Shimla has initiated a study on
exploring the role remote sensing for PF.
•Other institute in India initiated work on PF are:
-Central Potato Research Station – Jalandhar (Panjab)
Role of remote sensing in mapping the variability .
-MS Swaminathan Research Foundation- Chennai in
collaboration with NABARD has adopted a village in Dindigul
district of Tamilnadu for variable rate inputs application.

78. •Future prospects for PA include improvement in the
availability and performance of existing technologies.
•The most promising prospect in the future of PA is the
application of drones (the Aerial Frontier of Precision
Agriculture) towards the implementation of PA.
•In the light of tomorrow’s expected need and today’s
urgent requirement, PA needs to become the only
choice and not a choice in the field of agriculture.
Mehta and Masdekar, 2018

79. Figure- Future advancements in drone applications towards PA
source-
http://www.sk11.org/artikel/drones-for-agriculture-were-moving-ahead_243

80. In 2015, the Federal Aviation Administration approved the
Yamaha RMAX as the first drone weighing more than 55 pounds to
carry tanks of fertilizers and pesticides in order to spray crops.
ANDREW MEOLA,2020

81. Limitations for its implementation in developing
countries like India are :
•Small land holdings.
•Heterogeneity of cropping systems.
•Market imperfection.
•Complexity of tools and techniques requiring new skills.
•Lack of technical expertise.
•knowledge and technical gaps.
•Infrastructure and institutional constraints.
•High cost.

82. SUMMARY
•Research on Precision Farming is at infancy stage in
out country.
•Precision Farming technologies are successful in their
role of enhancing crop production, input use efficiency
while minimizing the cost of production and
environmental impacts.
•Precision land leveling, precision planting, real time N
application using LCC, SPAD (chlorophyll meter),
Green seeker sensor having demonstrated
potentialities for improving crop yield and increasing
resource-use efficiency in real farming situation.

83. •Tools and techniques for assessing soil and yield
variability for application of inputs need to be
standardized at a low cost and farmers friendly.
•Thus, Precision Farming may help farmers to harvest
through frontier technologies without compromising on
the quality of land and produce.
•The Precision Farming would trigger a techno-green
revolution in India which is the need of the hour.

85. Future Indian farmer

86. •Bana, R.C., Yadav, S.S., Shivran, A.C., Singh, P. and Kudi, V.K. 2020. Site-specific Nutrient Management for Enhancing Crop
Productivity. International Research Journal of Pure & Applied Chemistry. 21(15): 17-25.
•Banerjee, M., Bhuiya, G.S., Malik, G.C., Maiti, D. an Datta, S. 2014. Precision Nutrient Management through Use of LCC and
Nutrient Expert® in Hybrid Maize Under Laterite Soil of India. Universal Journal of Food and Nutrition Science 2(2): 33-36.
•Bongiovann,R.and Deboe, J.L.2004. Precision Agriculture and Sustainability. Precision Agriculture, 5: 359–387.
•Kumar, D.S. and Palanisami, K. 2010. Impact of Drip Irrigation on Farming System: Evidence from Southern India. Agricultural
Economics Research Review. 23: 265-272.
•Mehta, A. and Masdekar, M.2018. Precision Agriculture – A Modern Approach To Smart Farming. International Journal of
Scientific & Engineering Research 9(2): 23-26.
•Nalayani, P., Shankaranarayan, K. and Velmourougane. K. 2013. Herbigation in cotton (Gossypium spp): Effects on weed
control, soil microflora and succeeding greengram (Vigna radiata). Indian Journal of Agricultural Sciences 83 (11): 1144–8.
•Neupane, J. and Guo, W. 2019. Agronomic Basis and Strategies for Precision Water Management: A Review. Agronomy
2019, 9, 87; doi:10.3390/agronomy9020087
•Patle, G.T., Yadav S.R. and Pandey, V. 2018. Effect of irrigation levels and mulching on growth, yield and water use
efficiency of
•cauliflower and broccoli in perhumid ecoregion. An International Refereed, Peer Reviewed & Indexed Quarterly Journal in
Science, Agriculture & Engineering. VIII( XXV): 187-191.
•Qureshi A, Singh DK, Pandey PC (2016). Site-Specific Nutrient Management for Enhancing Nutrient-Use Efficiency in Rice and
Wheat. Acad. J. Agric. Res. 4(8): 518-524.
•Sayed, M.A. and Bedaiwy, M.N.A. 2011. Effect of Controlled Sprinkler Chemigation on WheatCrop in a Sandy Soil. Soil &
Water Res. 6(2): 61–72.
•Shah, N.G. and Das, I. 2014. Precision Irrigation: Sensor Network Based Irrigation.
https://www.researchgate.net/publication/221927923
•Seyfe, K. and Rashidi, M. 2007. Effect of Drip Irrigation and Plastic Mulch on Crop Yield and Yield Components of
Cantaloupe. International Journal Of Agriculture & Biology. 9(2): 247–249.
•Singh, B. and Ali, A.M. 2020. Using Hand-Held Chlorophyll Meters and Canopy Reflectance Sensors for Fertilizer Nitrogen
Management in Cereals in Small Farms in Developing Countries. Sensors. 20, 1127; doi:10.3390/s20041127
•Singh, D. and Tilak, D. 2020. A study on precision irrigation technology in agriculture: opportunities and challenges in Pune.
International Journal of Disaster Recovery and Business Continuity. 11(1): 405-422.
•Singh, V., Kaur, R., Singh, B., Brar, B.S. and Kaur, A. 2016. Precision Nutrient Management : A Review. Indian Journal of
Fertilisers. 12 (11): 1-15.
•Zayton, A.M., Guirguis, A.E. and Allam, KH. A. 2014. Effect of sprinkler irrigation management and straw mulch on yield,
water consumption and crop coefficient of peanut in sandy soil. Egypt. J. Agric. Res., 92 (2): 657-673.