Hindawi
Advances in Materials Science and Engineering
Volume 2022, Article ID 7793187, 10 pages
https://doi.org/10.1155/2022/7793187
Research Article
Application of Regression Analysis to Identify the Soil and Other
Factors Affecting the Wheat Yield
Azhar Hayat ,1 Muhammad Amin ,2 Saima Afzal ,1 Abdisalam Hassan Muse ,3,4
Omer Mohamed Egeh ,4 and Hafiz Saqib Hayat5
1
Department
Department
3
Department
4
Department
5
Department
2
of
of
of
of
of
Statistics, Bahauddin Zakariya University, Multan, Pakistan
Statistics, University of Sargodha, Sargodha, Pakistan
Statistics, Pan African University, Institute for Basic Sciences,Technology and Innovation, Nairobi, Kenya
Mathematics and Statistics, Amoud University, Borama, Somalia
Agronomy, Bahauddin Zakariya University, Multan, Pakistan
Correspondence should be addressed to Omer Mohamed Egeh; 1933@amoud.edu.so
Received 21 February 2022; Revised 12 April 2022; Accepted 19 April 2022; Published 10 May 2022
Academic Editor: Palanivel Velmurugan
Copyright © 2022 Azhar Hayat et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In farming and related fields, numerous connections exist that should be distinguished quantitatively. Several factors affect the
various crop yields in different dimensions. These factors may have relation with farmer’s practices or with quality of soil. In this
study, our main focus is to explore the effect of soil and other factors on the wheat yield. Regression modeling plays an important
role in the identification of such factors that greatly affect the crops yield. For reliable and valid results, one has to check the data
for outliers and other critical results. In this study, we have fitted the regression models with and without satisfying some
regression assumptions to determine the factors affecting yield of wheat. For analysis purposes, the required data were collected
from the district Multan. It was observed that when the regression assumptions were satisfied, then coefficient of determination
(R2) was improved from 45% to 48%, R2 (adjusted) was improved from 40% to 46%, and the standard error of the estimates was
reduced from 2.772 to 2.649. These results indicate that the soil characteristics, such as saturation, electrical conductivity, organic
matter, phosphorus, potassium, calcium carbonate, and micronutrients (zinc, copper, iron, manganese, and boron), are the
significant factors for wheat yield. While among all other factors, urea, chemical coating of seed, use of compost, and previously
sown crops are the significant factors for wheat yield.
1. Introduction
The agriculture sector plays a key role in the economy of
Pakistan as it contributes 19.2% to gross domestic product
(GDP) and 38.5% of the labor force is engaged in this sector
[1]. It also has a major contribution to the foreign exchange
earnings of the country and the development of other
sectors. However, nowadays, Pakistan is facing the issues of
water stress, pollution, and environmental degradation
along with institutional and socio-economic problems; these
issues have severely affected the agricultural productivity of
the country. Consequently, there is a need for strong
strategies and their implementation to improve productivity
in the agriculture sector [2].
Wheat is the main food crop of Pakistan, and it dominates all crops in production. According to world population review (2022), Pakistan is ranked at seventh place
among the highest wheat-producing countries in the world
in terms of yield per acre, area, and production. The mean
per capita consumption of wheat in Pakistan is about 125 kg
per year and is about 60% of daily food for an average person
[3].
The earnings of almost 80% of the farmers in Pakistan
are dependent on wheat. It is grown at an area of 9 million
hectares that is roughly 40% of the total cultivated land in the
country. The wheat crop added about 8.7% to the total value
of agriculture and contributes about 2.1% to the GDP [4].
Wheat is considered a staple crop for the countries in
2
temperate zones and is increasing in demand in the
countries that are undergoing industrial and urban development. It is the main source of starch and energy, further it
provides essential nutrients like vitamins, dietary fiber, and
proteins, which are necessary for human growth [5]. That is
why most researchers are strenuous to know factors that
increase and decrease wheat production due to its high
importance.
Worldwide, several analysts examined the impact of
various factors on the yield of wheat. Barzegar et al. [6]
investigated the impact of wheat straw, composted sugarcane residue on properties, and yield of wheat in Iran. They
applied the split-plot design and found that organic materials increased wheat yield, aggregate stability, water retention, and decrease soil bulk density. Martre et al. [7]
proposed mechanistic hypotheses for grain nitrogen accumulation in cereal. The four cultivars were tested in France.
The proposed hypotheses were tested with the help of linear
regression. It was found that under normal conditions,
Arche and Rectal cultivars had the highest grain per year
while Tamaro cultivar had the lowest grain per year. Plaut
et al. [8] conducted experiments on Suneca and Batavia
wheat varieties in Australia. A mechanistic model was developed and showed that kernel number was not affected by
water deficit, but high temperature caused a significant
reduction in both varieties. Water deficit and the high
temperature were also found to increase the relative contribution of transported dry matter to kernels. Leilah and AlKhateeb [9] explored the association between wheat yield
and its components under drought conditions in Saudi
Arabia. Multiple statistical techniques including correlation,
linear regression, path analysis, factor analysis, cluster
analysis, and principal components analysis were used and it
was revealed that weight of grain, number of spikes per
square meter, and biological yield had the most influence on
wheat grain yield indicating that breeding material with
these qualities can produce a high yield in drought conditions. Moriondo et al. [10] adapted the use of Normalized
Difference Vegetation Index (NDVI) data obtained from the
simulation model and satellite platforms for the estimation
of wheat yield. This methodology was applied in two high
wheat-yielding provinces of Italy. They found that the
proposed methodology produced a better accuracy in the
estimation of wheat yield. Khan et al. [11] studied the effect
of zinc fertilizer at different levels on the yield of wheat in
Multan, Pakistan. The difference among these treatments
was compared using the least significant differences (LSD),
polynomial curve fitting, and coefficient of determination,
and it was found that application of 5 kg ha−2 of zinc sulphate gave the highest marginal rate of return as compared
to other amounts of application of zinc sulphate. Whalley
et al. [12] discussed the relation between soil strength and
yield of wheat for two different soil types in England. They
applied the factorial design and found that soil strength is a
good predictor of crop yield irrespective of soil type, water
status, and concluded that soil strength seemed to limit crop
productivity. Abbas et al. [13] evaluated the influence of
trace elements in nitrogen, phosphorus, and potassium
absorption on wheat yield. With the help of randomized
Advances in Materials Science and Engineering
complete block design (RCBD) and Duncan’s multiple range
test, they showed that limited use of iron increased wheat
yield. If the application of iron was increased, the effect on
the yield was insignificant or undesirable. Hassan et al. [14]
conducted a study to find significant factors that were
influencing the wheat yield in mixed cropping zones of
Punjab. The data were collected from four different districts
of Punjab. They applied the linear regression and found that
seed rate, cost weedicide that was used, education of farmer,
use of nitrogenous fertilizer, rotavator use, and sowing time
are the significant factors for wheat yield. Gul et al. [15]
explored the effect of foliar application of micronutrients on
Ghazanive-98 variety of wheat in Peshawar, Pakistan. They
applied the RCBD and found that foliar treatment of
micronutrients affected the growth of the wheat variety
without causing any effect on the time of growth.
Nadim et al. [16] evaluated the yield characteristics and
physiology of the Gomal-8 variety of wheat for different
levels of micronutrients in Dera Ismail Khan. With the help
of RCBD, they found that the application of Boron produced
a higher leaf area index while the application of copper
produced a maximum number of tillers causing an increase
in wheat yield. Rezaei and Hemati [17] studied the effect of
soil properties on wheat yield in Iran. They applied the
RCBD, and the results showed that a balanced percentage of
sand, clay, and silt provide favorable conditions for the
improvement of wheat yield. El-Lethy et al. [18] discussed
the potassium impacts on wheat plants under saline conditions in Giza, Egypt. With the help of analysis of variance
(ANOVA) and LSD, they concluded that the yield of wheat is
decreased significantly in saline conditions, and potassium
fertilizer reduces the undesirable effects of salinity. Alam and
Salahin [19] conducted a series of experiments to study the
effect of soil density and moisture retention on wheat yield in
Bangladesh. They applied the multiple regression models
and found that wheat yield almost doubled from lowest soil
depth to highest soil depth. Muarya et al. [20] conducted a
field experiment during the Rabi season in Kanpur to study
the influence of potassium levels on wheat yield and growth.
Using factorial RCBD, it was observed that the application of
80 kg ha−1 of potash (K2O) produced the highest grain yield,
straw yield, and biological yield compared to 0, 40, and 60 kg
ha−1 application of K2O. Limon-Ortega and Martinez-Cruz
[21] studied the impact of nitrogen on wheat yield in Mexico.
The data were analyzed through ANOVA. It was found that
nitrogen sources impacted wheat yield and the number of
spikes based on soil reaction while the fungicide spray had a
positive influence on the wheat yield.
Ghadikolayi et al. [22] studied the influence of crop
residue and nitrogen on wheat yield in Iran. They applied the
RCBD and found that 135 kg/ha of nitrogen gave the highest
soil organic matter (OM). It was also found that all of the
residues used in the experiment reduced the yield of wheat
but the reduction with the use of sunflower residue was
lowest compared to other crop residue tested. Sarto et al.
[23] investigated the effects of silicate application on soil
chemical properties in Parana, Brazil. With the help of
regression models and it was concluded that the calcium/
magnesium silicate in acid clayey soil improves the yield of
Advances in Materials Science and Engineering
wheat. However, the soil with pH higher than 5.3 and high
silicon does not impact the grain yield of wheat.
Arshad et al. [24] conducted an experiment in Peshawar,
Pakistan, to study the interactive impact of zinc and
phosphorus on wheat. With the help of LSD test, it was
found that 5 kg ha−1 of zinc gave maximum straw yield, but
10 kg ha−1 zinc significantly increased all other indicators of
yield. As for phosphorus, 90 kg ha−1 was observed to produce the best results. Mehmood et al. [25] studied the input
factors that positively influence wheat production in
Bahawalnagar, Punjab. The researchers used the linear regression and found that the sowing method, use of fertilizer,
that is, nitrogen and phosphorus, variety of wheat, use of
weedicides spray and irrigation mode have a significant
effect on the wheat yield. Chairi et al. [26] conducted a study
on the genetic gain in yield for durum wheat in three experiment stations in Spain. They considered the linear regression to determine variability in locations for absolute
and relative genetic gain (AGG and RGG). The result of the
analysis showed that the rate of genetic progress in durum
wheat yield has been low in the past decade. Rajicic et al. [27]
carried out tests on wheat plants in soil with low pH by
applying nitrogen along with phosphorus and potassium
fertilizers in Serbia. They applied correlation analysis and
found that nitrogen had a significant impact on wheat yield
and the treatments where the highest amount of nitrogen
was applied with other combinations of phosphorus, and
potassium had a high yield compared to the lower application of nitrogen. Polisetty and Paidipati [28] estimated the
trends in the production of wheat using data from four states
of India. The trend analysis was conducted using nonparametric methods including Pettitt’s Standard Normal
Homogeneity, Buishand’s range test, and Mann Kendall test.
The outcomes of the trend analysis showed that all states
under consideration had an upwards trend and indicated an
improvement in wheat production. Sial et al. [29] investigated the effect of waste-derived-biochars of milk tea and
fruit peels on growth, yield, root traits, soil enzyme activity,
and nutrient status of the wheat crop in Shaanxi, China.
Eight treatments were analyzed with one-way ANOVA and
it was found that plant height, dry weight of root and shoot,
chlorophyll amount, grain yield significantly increased using
the treatment of milk tea biochar and chemical fertilizer.
Ashraf et al. [30] analyzed the input-output flow for wheat
production to identify energy-efficient ways through data
from Mailsi, Pakistan. It was found through multiple regression models that higher inputs, large fields, high fertilizer application, and tillage operation provided the highest
energy outputs with high productivity and efficiency in
energy. Zhou et al. [31] studied the relationship between the
depth distribution of wheat roots and soil macroporosity in
United Kingdom with six varieties of wheat. Two-way
ANOVA was used, and it was determined that there was no
significant difference in wheat genotypes, and the wheat root
system was more affected by the soil macropore system.
Recently, Hayat et al. [32] explored the effect of soil
properties and other factors on the cotton yield of Pakistan.
They observed that EC, pH, saturation, OM, P, Zn, Cu, Fe,
and B are the significant factors for cotton yield. They also
3
found that fertilizer (Nitrophos, nitrogen, and urea), previously sown crops (wheat and corn), type of seed, chemical
coating of seed, type of water, way of cultivation, and use of
compost are also a significant factors for cotton yield.
As we have seen in the literature that worldwide, mostly
researchers studied the effect of individual soil characteristic
or two or other factors on the wheat yield separately. No one
still studied the joint influence of soil characteristics and
other factors on the wheat yield. So, in this study, our main
focus is to explore the joint influence of these factors on the
wheat yield. To explore the influence of these factors on the
wheat yield, we will consider the linear regression model and
will identify which factors contribute a significant role in the
wheat yield. Moreover, we will also evaluate some of the
regression assumptions to obtain the reliable results. These
assumptions include no multicollinearity, constant error
variance, no autocorrelation, no outlier, and influential
observation [33, 34]. In this article, we have paid special
attention to the regression model diagnostics and its impact
on the wheat yield model for the identification of factors.
Regression diagnostics include outliers and influential observations analysis which can affect the model estimates and
predicted values. With the presence of these values, the fitted
model results may indicate the significance of the factors
which are playing no role in the response variables and vice
versa.
2. Materials and Methods
The major food producing province of Pakistan is Punjab
and is titled as “bread basket” to feed more than 220 million
Pakistanis. Wheat is the main cereal crop produced in
Punjab.
The investigation zone is in the bond that is made by five
rivers of Punjab situated at 30.157 degrees North and 71.524
degrees South with an average elevation of 122 m above
ocean level. The region as a zone of about 132.1 km square is
partitioned into various tehsils. The normal temperature in
the area fluctuates from 20°C to 45°C with normal precipitation of 175 mm a year. This demonstrates that a particular
crop cannot be planned to cultivate in the soil for the entire
year in the area because climate conditions change drastically. The conditions of this zone in winter are ideal for
wheat production.
The population size is 2620 acres of this area, where one
acre is taken as a single sampling unit. The sample was taken
as 655 and computed using appropriate formula. Systematic
random sampling technique was used to collect the soil
samples. The sample was collected from every fourth acre
using 4 as sampling interval.
It is necessary to maintain the specific concentrations of
organic and inorganic matter in the soil for a good yield of
crops. Five hundred grams soil was taken from each selected
acre of land to measure the variables specified in Table 1.
Then these samples were sent to laboratory of the Agriculture Department of Punjab under Agri-Smart project for
the computations of the soil characteristics, where they used
the several apparatus. The details of apparatus to measure
variables related to soil characteristics are presented in
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Advances in Materials Science and Engineering
Table 1: Apparatus for measuring the soil characteristics
Sr. No
Variable
1
Saturation percentage
2
Electrical conductivity
3
Power of hydrogen
4
Organic matter
5
Phosphorus
6
Potassium
7
Calcium carbonate
8
Zinc
9
Copper
10
Iron
11
Manganese
12
Boron
Apparatus
1. Polythene sheets
2. Spade
3. Soil auger
4. Moisture boxes/cans
5. Balance
6. Oven
7. Ring stand
8. Funnel (glass or plastic)
9. Tubing (to attach to bottom of funnel)
10. Clamp (to secure tubing)
11. Filter paper (to line funnel)
12. Beakers (250 mL)
13. Graduated cylinder
14. Stirring rod (long)
1. Conductivity bridge
2. Vacuum filtration system
1. pH meter with combined electrode
2. Beakers: Preferably use polyethylene or TFE beakers
3. Mechanical stirrer, with inert plastic coating
4. Wash bottle, plastic
1. Magnetic stirrer and teflon-coated magnetic stirring bar
2. Glassware and pipettes for dispensing and preparing reagents
3. Titration apparatus (burette)
1. Spectrophotometer or colorimeter
2. Standard laboratory glassware: beakers, volumetric flasks, pipettes, and funnels
1. Flame photometer with accessories
2. Beakers
3. Pipettes and volumetric flasks, as required for dilution and tests of interference effects
1. Hot plate
2. Burette
3. Erlenmeyer flask
4. Volumetric pipette
1. Atomic absorption spectrophotometer
2. Mechanical shaker, reciprocal
1. Atomic absorption spectrophotometer
2. Mechanical shaker, reciprocal
1. Atomic absorption spectrophotometer
2. Mechanical shaker, reciprocal
1. Atomic absorption spectrophotometer
2. Mechanical shaker, reciprocal
1. Porcelain crucibles
2. Spectrophotometer
3. Polypropylene test tubes
Table 1. The factors estimated for each sample of soil attributes incorporate OM, phosphorous (P), potassium (K),
and calcium carbonate (CC). The significant micronutrients
in the soil that were estimated from each sample include zinc
(Zn), copper (Cu), iron (Fe), manganese (Mn), and boron
(B).
The information related to the utilization of fertilizer,
such as DAP, potash, urea, Nitrophos, and nitrogen manures
in the land per section of land is also collected from the
farmers. The data about the recurrence of pesticides and
water systems and strategy for the water system, that is, tubewell water or trench water were gathered. The information
about the crop sown before wheat, seed type, that is, coated
or uncoated, the technique for cultivating, that is, penetrating or drilling sewing strategy, and utilization of fertilizer
in the land were also collected. Further details and descriptions about these factors are given in Table 2.
3. Results
Descriptive analysis of all considered variables is given in
Table 3. The descriptive statistics include average, standard
deviation, coefficient of variation, maximum, minimum,
range skewness, and Kurtosis. The average wheat yield in this
area is found to be 41.87 mund/acre with a standard deviation of 3.66. The average saturation is 37.75% which
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Table 2: Description of wheat yield and its associated factors.
Name of variable
Saturation (%)
Electrical conductivity (dsm-1)
pH
Organic matter (%)
Phosphorus
Potassium
Calcium carbonate (%)
Zinc
Copper
Iron
Manganese
Boron
Nitrophos
Nitrogen
DAP
Potash
Urea
Last crop
Water frequency
Pesticide frequency
Chemical coating of seed
Type of water
Way of cultivation
Usage of compost
Wheat yield per acre
Notation
—
EC
—
OM
P
K
CC
Zn
Cu
Fe
Mn
B
—
—
—
—
—
—
—
—
—
—
—
—
—
Nature of variable
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Discrete (frequency)
Discrete (frequency)
Discrete (frequency)
Discrete (frequency)
Discrete (frequency)
Discrete (frequency)
Discrete (frequency)
Discrete (frequency)
Categorical
Categorical
Categorical
Categorical
Continuous
Unit of variable
—
—
—
—
Ppm
Ppm
—
Ppm
Ppm
Ppm
Ppm
Ppm
50 kg per acer
50 kg per acer
50 kg per acer
50 kg per acer
50 kg per acer
—
—
—
—
—
—
—
Munds
Note: Ppm � “parts per million”.
Table 3: Descriptive statistics of the consider characteristics.
Variables
Wheat yield
Saturation (%)
EC (dsm-1)
pH
Org. M. (%)
Phosphorus (ppm)
Potassium (ppm)
CC (%)
Zinc (ppm)
Copper (ppm)
Iron (ppm)
Manganese(ppm)
Boron (ppm)
Nitrophos
Nitrogen
DAP
Potash
Urea
Water frequency
Pesticide frequency
Average
41.87
37.75
4.24
8.26
0.55
8.20
176.10
6.36
0.78
0.18
4.08
0.89
0.46
0.41
0.51
1.01
0.31
1.92
3.40
1.93
SD
3.66
3.82
3.97
0.29
0.15
3.26
74.81
1.43
0.22
0.02
0.68
0.15
0.01
0.58
0.71
0.50
0.49
0.68
0.70
0.82
CV
0.09
0.10
0.94
0.03
0.27
0.40
0.42
0.23
0.28
0.11
0.17
0.17
0.03
1.41
1.40
0.50
1.60
0.35
0.21
0.43
Minimum
29.00
21.00
0.20
7.20
0.12
1.40
78
2.80
0.10
0.05
0.42
0.07
0.39
0.00
0.00
0.00
0.00
0.00
2.00
1.00
means that the soil of this area is loam which indicated that
this soil is suitable for wheat. The average value of EC is 4.24
dS/m depicting that there is slight saline soil which is a
favorite for wheat. The average pH of the soil is 8.26 indicating that it is difficult for the plant to obtain phosphorus
from the soil. The average amount of OM in the soil is 0.55%,
which shows that there is a lack of organic components in
the soil. The average amount of phosphorus in the soil is
Maximum
50.00
45.00
22.00
9.00
1.09
23.50
380.00
13.20
1.00
0.32
4.93
1.87
0.49
2.00
3.00
3.00
2.00
4.00
5.00
6.00
Range
21.00
24.00
21.80
1.80
0.97
22.10
302
10.40
0.90
0.27
4.51
1.80
0.10
2.00
3.00
3.00
2.00
4.00
3.00
5.00
Skewness
−1.41
−4.81
22.18
−1.13
3.73
13.90
1.081
5.37
−12.34
−10.10
−25.64
−20.26
−6.93
11.16
11.97
1.65
12.42
0.14
1.61
7.32
Kurtosis
−2.00
−1.98
25.53
−2.66
1.51
14.43
−0.058
6.84
0.86
59.30
36.10
51.24
2.83
0.78
1.68
8.23
1.08
3.37
−0.81
2.54
8.20 ppm which is normal. The average amount of potassium
in the soil is 176.10 ppm which is good for the soil to be
called fertile. The average zinc is about 0.78 ppm in the soil
indicating an intermediate amount of the nutrient. The
average copper in the soil was found to be 0.18 ppm, which is
a satisfactory amount for the fertility of the soil. The average
iron in the soil is 4.08. This indicates the soil’s high fertility.
The manganese in the soil on average is 0.89 ppm which is a
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Advances in Materials Science and Engineering
Type of Water
2.8%
Previously Sown Crops
6.1%
3.4%
12.1%
39.9%
59.2%
19.3%
Cotton
Rice
Corn
57.3%
Cannal water
Tube Well
Mixture of Cannal and Tube Well
Sugarcane
Empty
Figure 1: Pie chart for previously sown crops.
Figure 3: Pie chart for the type of water.
Chemical Coating of Seed
Wheat Sowing Methods
11.0%
10.7%
45.7%
43.6%
Gaup Chatt Method
Watar Kashat Method
Drilling Method
89.0%
Figure 4: Pie chart for wheat sowing methods.
Yes
No
Figure 2: Pie chart for chemical coating of a seed.
good amount for the fertility of the soil. Finally, the average
amount of boron in the soil is 0.46 ppm indicating enough
nutrients in the soil. The skewness of most data distribution
was positive, and the highest values of the skewness coefficients were EC and phosphorus. Visconti et al. [33]
studying soil saturation extracts in Spain, also found higher
positive skewness for the soil potassium and attributed this
to the fact that fertilizers may have been applied at a higher
concentration at some locations. Negative skewness was
observed in some factors such as iron (ppm), manganese
(ppm), and zinc (ppm).
The summary of some qualitative factors is shown in
Figures 1 to 5. Figure 1 shows that cotton was the most
common previously sown crop as compared to other crops.
From Figure 2, we have found that most of the seeds were
chemically coated. Figure 3 displays that most farmers use
Usage of Compost
24.9%
75.1%
Yes
No
Figure 5: Pie chart for using compost.
Advances in Materials Science and Engineering
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Table 4: Regression analysis with and without outlier.
Term
Constant
Saturation (%)
EC (dsm-1)
pH
Org. M. (%)
Phosphorus (ppm)
Potassium (ppm)
CC (%)
Zinc (ppm)
Copper (ppm)
Iron (ppm)
Manganese (ppm)
Boron (ppm)
Nitrophos
Nitrogen
DAP
Potash
Urea
Water frequency
Pesticide frequency
Last crop
Rice
Corn
Sugarcane
Empty
Seed chemical coating
No
Water type
Tube well
Both (tube well and canal water)
Way of cultivation
Water kashat method
Drilling method
Compost use
No
Full data
SE
T
6.2800
11.04
0.0335
−4.59
0.0297
2.44
0.4440
5.28
0.7920
1.83
0.0388
−6.78
0.0019
−5.18
0.0829
−5.96
0.5950
−7.97
5.7700
−1.86
0.1670
−2.02
0.7890
−2.71
9.2800
−5.95
0.2110
0.44
0.1690
0.83
0.2430
−0.48
0.2360
−0.22
0.1750
1.70
0.1680
−1.01
0.1360
1.02
P value
0.0000
0.0000
0.0150
0.0000
0.0670
0.0000
0.0000
0.0000
0.0000
0.0630
0.0440
0.0070
0.0000
0.6610
0.4070
0.6310
0.8270
0.0900
0.3110
0.3080
Beta
68.420
−0.145
0.080
2.340
1.862
−0.285
−0.008
−0.558
−4.892
−11.500
−0.357
−1.936
−52.210
0.001
0.069
−0.188
0.036
0.525
−0.277
0.279
0.785
0.299
0.840
0.591
0.2940
0.3680
0.6280
0.4760
2.67
0.81
1.34
1.24
0.0080
0.4170
0.1820
0.2150
0.713
0.493
0.791
0.542
0.2840
0.3590
0.6010
0.4560
2.51
1.37
1.32
1.19
0.0120
0.1700
0.1890
0.2350
−1.031
0.3700
−2.79
0.0050
−0.959
0.3580
−2.68
0.0080
−0.172
−0.350
0.6960
0.7000
−0.25
−0.50
0.8040
0.6170
−0.054
−0.229
0.6660
0.6690
−0.08
−0.34
0.9360
0.7320
0.009
0.369
0.2410
0.3940
0.04
0.94
0.9700
0.3490
0.003
0.308
0.2340
0.3830
0.01
0.8
0.9900
0.4230
−1.218
0.2570
−4.74
0.0000
Beta
69.350
−0.154
0.072
2.345
1.454
−0.263
−0.010
−0.494
−4.740
−10.740
−0.337
−2.137
−55.210
0.092
0.140
−0.117
−0.052
0.298
−0.171
0.139
2
2
F (P value) � 17.75 (0.0000), S � 2.772, R � 45.24%, R (adj) � 40%
tube-well water in their wheat fields. Figure 4 indicates that
most of the farmers use Gaup Chatt sowing method to grow
wheat in this area. From Figure 5, we have found that most
farmers did not use compost in their fields before sowing
wheat.
After the analysis of descriptive statistics, the next step
was to identify the contribution of significant variables by
fitting a linear regression model to the data of the soil
chemical and other characteristics which influence the wheat
yield. The results obtained from the linear regression model
on full data showed that R2 of the fitted model explained
45.24% of the variability in wheat yield per acre due to the
considered soil and other characteristics. The results of the
F-test given in Table 4 show that considered variables
contribute a 45.24% role in the wheat yield per acre. The
adjusted R-squared statistic, which is a more suitable safeguard, used for not overfitting of the regression model with
multiple factors, was 40%. The role of the individual factors
After deleting
SE
6.0800
0.0324
0.0288
0.4330
0.7730
0.0375
0.0019
0.0804
0.5810
5.6600
0.1610
0.7580
8.9500
0.2020
0.1640
0.2360
0.2300
0.1970
0.1630
0.1430
outliers
T
11.26
−4.46
2.76
5.41
2.41
−7.6
−4.33
−6.93
−8.43
−2.03
−2.22
−2.55
−5.83
0.01
0.42
−0.8
0.16
2.66
−1.7
1.96
P value
0.0000
0.0000
0.0060
0.0000
0.0160
0.0000
0.0000
0.0000
0.0000
0.0430
0.0270
0.0110
0.0000
0.9950
0.6730
0.4250
0.8750
0.0080
0.0900
0.0510
−1.167
0.2470
−4.72
0.0000
F (P value) � 19.59 (0.0000), S � 2.649,
R2 � 48.38%, R2 (adj) � 45.91%
in the wheat yield was also explored. To determine the
contribution of variables and identification of most significant variables, we use the t-test, and the results are reported
in Table 4. It can be seen that saturation (%), EC, pH,
phosphorus, potassium, CC, zinc, iron, manganese, and
boron were found to be statistically significant factors at a 5%
level of significance. Of these factors, zinc was found to be
the most significant factor for wheat yield (see t values and its
associated p values). The t value on absolute for this factor is
larger than the t values of the other considered factors, and it
is already indicated in the literature that zinc contributes a
significant role in various crop yields [35–38]. The secondmost important factor is soil phosphorous, which contributes a significant role to increase or decrease the wheat yield
per acre. The factor previously sown crops (rice), chemical
coating of seed (no), and use of compost (no) are statistically
significant factors that are increasing the wheat yield. The
nutrients organic matter and copper respectively are not play
8
Advances in Materials Science and Engineering
Table 5: Residual analysis for the regression based on full data.
Observation number Wheat yield
27
38
82
36
135
38
153
35
213
47
302
47
315
37
319
36
366
35
368
38
370
38
388
36
477
50
506
37
517
35
533
38
614
38
617
36
Fit
43.32
41.308
43.36
40.494
41.73
41.454
42.057
41.725
40.427
43.278
43.521
41.558
44.39
42.438
40.519
43.473
43.517
41.291
Resid Std resid
−5.32
−2.01
−5.308
−2.02
−5.36
−2.05
−5.494
−2.06
5.27
2.01
5.546
2.03
−5.057
−2.02
−5.725
−2.11
−5.427
−2.01
−5.278
−2.01
−5.521
−2.05
−5.558
−2.09
5.61
2.07
−5.438
−2.0
−5.519
−2.03
−5.473
−2.08
−5.517
−2.05
−5.291
−2.03
a significant role in the growth of wheat yield. As well as the
impact of some fertilizers namely Nitrophos, nitrogen, DAP,
potash, and urea are not beneficial to some extent as
compared to the factors discussed earlier.
The regression results are reliable if there, is no outlier in
the residual. Checking the presence and identification of
outliers is the next step. For this purpose, we used standardized residuals. The evaluation of these residuals is given
in Table 5. Table 5 indicates that the observation number 27,
82, 135, 153, 213, 302, 315, 319, 366, 368, 370, 388, 477, 506,
517, 533, 614, 617 are identified as outlier.
After identification of these outliers, the output showed
the results of a linear regression model to describe the relationship between wheat yield per acre and 13 independents
(qualitative and quantitative) variables. Since the p value of
the F-test in Table 4 is less than 0.05, there was a statistically
significant relationship between the wheat yield and associated factors with the exclusion of substantial values observations. The R2 indicated that the model as fitted
explained 48.38% of the variability in wheat yield per acre.
The adjusted R-squared statistic, which was more suitable for
comparing models with different numbers of independent
variables, was 45.91%. The standard error of the estimate
showed the standard deviation of the residuals to be 2.649
which was smaller as compared to model fitted variability
with full data. Now we explore the role of the individual
factors in the wheat yield. It can be seen from Table 4 that
saturation (%), EC, pH, organic matter, phosphorus, potassium, CC, zinc, copper, iron, manganese, boron, and urea
were found to be statistically significant factors at a 5% level
of significance. One more thing is noted, the role of organic
matter, copper, and urea was hidden in the full data set due
to the outliers, but after removing the outliers, they are
statistically contributing a significant role in the wheat yield.
Now we discuss the effect of farmer-related factors on
wheat yield. One factor that is previously sown crop where
several crops were indicated. Of these previously sown crops,
rice was the most significant as compared to other previously
sown crops which increases the wheat yield. Other previously sown crops also increases wheat yield but not significantly. The second-most important farmer-related factor
is chemical coating of the wheat seed which also contributes
a significant role in the wheat yield. From Table 4, we observed that the wheat yield of the chemical coating seed was
maximum as compared to the wheat yield where the seed is
not chemical coated. The use of compost also increases
wheat yield. Results indicate that the wheat yield of the land
was higher where compost was used than where it was not
used. In the similar way, we also study the impact of some
fertilizers namely urea, Nitrophos, nitrogen, DAP, and
potash on the wheat yield. Table 4 results show that urea
fertilizer has a direct influence on the wheat yield while other
types of fertilizers are not beneficial to some extent of this
land area. Furthermore, water frequency has negative impact
on wheat yield, which means that this crop need small water
frequency. If we increase the water frequency, then wheat
yield may be decreased. Pesticide frequency also has a direct
impact on wheat yield. This indicated that several times use
of pesticides increases the wheat yield.
After removing all outliers, the results of the fitted model
as shown in Table 4 were entirely different as compared to
the results of the model fitted to the full data model. On
comparing the fitted models as given in Table 4, significant
results satisfying the regression assumption outliers were
found. It was found that the R2 and adj R2 (Adjusted) are
increased from (45.24% to 48.38%) and (40% to 45.91%),
respectively (Table 4). The standard error of the estimates
decreased from (2.772 to 2.649) and indicated better results
with satisfying regression assumptions.
4. Discussion
All previous studies for the wheat yield model did not give
any attention to regression assumptions and all these factors
simultaneously. This study focused on the effect of outliers
on the regression analysis to identify the factors which affects
the wheat yield. Here, we also studied the effect of outliers on
the regression assumptions. We have seen that outliers have
strongly affected the model estimates. Also, it can be observed that insignificant factors have turned out to be significant after deleting all these outlying values. After
considering the regression assumptions, the significant role
of organic matter, copper, and urea from fertilizers can be
observed. These variables were insignificant when regression
assumptions were not satisfied. These results coincide with
previous studies [35–42].
The outliers may also be the cause of multicollinearity,
heteroscedasticity, and autocorrelation. It is also observed
that after deleting outlying observations, the multicollinearity, heteroscedasticity, autocorrelation, and error
variance were reduced substantially. Based on our results, it
is found that saturation (%), EC, pH, phosphorus, potassium, CC, zinc, iron, manganese, boron, chemical coating of
seed, previously sown crops are the significant factors to
change the wheat yield. Based on these results, it may be
suggested that in all agriculture-related fields, the
Advances in Materials Science and Engineering
identification and exclusion of outliers is a crucial step for
obtaining better results of regression analysis.
5. Conclusion, Limitations, and
Future Research
There are various factors which affect the crops yields.
These factors include soil characteristics and farmer-related characteristics. As our interest is to identify the soil
and other factors which influence the wheat yield. Also
identify which one factor(s) are most critical to increase
wheat yield which can be helpful for farmer. For the
identification of such factors, we considered the regression analysis with the evaluation of outliers. When we
ignore the presence of outlier, then the role of some
important factors is found to be statistically insignificant.
When we consider the outlier and exclude from the data,
then these insignificant factors now indicate the significant role in the wheat yield. From the results, we found
that saturation (%), EC, pH, phosphorus, potassium, CC,
zinc, iron, manganese, boron, chemical coating of seed,
and previously sown crops are the significant factors to
change the wheat yields. Of these significant factors, more
important six factors with order of importance are zinc,
phosphorous, CC, pH, boron, and saturation.
This study considered some factors related to wheat
yield, there may be other factors which may be related to
wheat yield. These factors may be seed variety, land type,
bushels, and so on. As our interest was mainly on soil
characteristics and some other additional factors, we are
unable to collect the information about these factors. This
study can also be extended for other crops, such as maize,
rice, and others to identify soil related factors and influence
on yield. Moreover, such analysis can also be consider by
considering some other regression models such as generalized linear models, nonlinear models, and nonparametric
regression model.
Data Availability
The data used to support the findings of this study are included within the article.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
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