Optimizing the Phosphorus Use in Cotton by Using CSM-CROPGRO-Cotton Model for
Semi-Arid Climate of Vehari-Punjab, Pakistan
Asad Amin1, Wajid Nasim1,2,3,*, Muhammad Mubeen1, Muhammad Nadeem1, Liaqat Ali4, Hafiz Mohkum
Hammad1, Syeda Refat Sultana5, Khawar Jabran6 , M. Habib ur Rehman5,7, Shakeel Ahmad8, Muhammad
Awais9, Atta Rasool10, Shah Fahad11*, Shah Saud12, Adnan Noor Shah13, Zahid Ihsan14, Shahzad Ali15, Ali Ahsan
Bajwa16, Khalid Rehman Hakeem17, Asif Ameen18 , Amanullah19, Hafeez Ur Rehman20, Fahad Alghabar14,
Ghulam Hussain Jatoi21, Muhammad Akram1, Aziz Khan11, Faisal Islam22, Syed Tahir Ata-Ul-Karim23,
Muhammad Ishaq Asif Rehmani24,Sajid Hussain25, Muhammad Razaq26, Amin Fathi27
1
Department of Environmental Sciences, COMSATS Institute of Information Technology (CIIT), Vehari,
Pakistan
CIHEAM-Institut Agronomique Méditerranéen de Montpellier (IAMM), 3191 route de Mende, Montpellier,
France
2
3
CSIRO Sustainable Ecosystems, National Research Flagship, Toowoomba, Qld 4350, Australia
4
Adaptive Research Farm, Punjab Agriculture Department, Vehari, Pakistan
5
Department of Agronomy, Muhammad Nawaz Shareef University of Agriculture, Multan, Pakistan
6
Department of Plant Protection, Faculty of Agriculture and Natural Sciences, Düzce University, Düzce, Turkey
7
AgWeatherNet Program, Washington State University, Prosser, WA 99350-8694, USA
8
Department of Agronomy, Bahauddin Zakariya University, Multan, Pakistan
Department of Agronomy, University College of Agriculture and Environmental Sciences, The Islamia
University of Bahwalpur-Pakistan
9
10
State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of
Sciences, Guiyang 550081, China
11
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
12
Department of Horticulture, Northeast Agricultural University, Harbin, 150030, China
MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze
River, College of Plant Sciences and Technology, Huazhong Agricultural University, Wuhan 430070, China
13
14
Department of Arid Land Agriculture, Faculty of Meteorology, Environment & Arid Land Agriculture, King
Abdul Aziz University, Jeddah 21589, Saudi Arabia
15
The Chinese Institute of Water-saving Agriculture, Northwest A&F University, Yangling 712100, Shaanxi,
China
16
Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland,
Toowoomba 4350, QLD, Australia
17
Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi
Arabia
18
College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
19
Department of Agronomy, Faculty of Crop Production, The University of Agriculture, Peshawar 25130,
Pakistan
20
Department of Crop Physiology, University of Agriculture, Faisalabad
21
Department of Plant Pathology, Sindh Agriculture University, Tandojam, Hydrabad, Sindh, Pakistan.
22
Institute of Crop Science and Zhejiang Key Laboratory of Crop Germpalsm, Zhejiang University, Hangzhou
310058, China
23
National Engineering and Technology Center for Information Agriculture,
Nanjing Agricultural University, 1 Weigang Road, Nanjing, Jiangsu 210095, P. R. China
1
24
Department of Agronomy, Ghazi University, Dera Ghazi Khan, Pakistan
25
China National Rice Research Institute, Hangzhou, China
26
School of Forestry, Northeast Forest University, Harbin, China
27
Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran
*Corresponding
authors:
Wajid
Nasim
([email protected]);
([email protected] or [email protected])
Shah
Fahad
Abstract
Crop nutrient management is an essential component of any cropping system. With increasing concerns over
environmental protection, improvement in fertilizer use efficiencies has become a prime goal in global
agriculture system. Phosphorus (P) is one of the most important nutrients and strategies are required to optimize
its use in important arable crops like cotton (Gossypium hirsutum L.) has great significance. Sustainable P use in
crop production could significantly avoid environmental hazards resulting from over-P fertilization. Crop
growth modeling has emerged as an effective tool to assess and predict the optimal nutrient requirements for
different crops. In present study, Decision Support System for Agro-technology Transfer (DSSAT) sub-model
CSM-CROPGRO-Cotton-P was evaluated to estimate the observed and simulated P use in two cotton cultivars
grown at three P application rates under the semi-arid climate of southern Punjab, Pakistan. The results revealed
that both the cultivars performed best at medium rate of P application (57 Kg ha-1) in terms of days to anthesis,
days to maturity, seed cotton yield, total dry matter production and harvest index during 2013 and 2014.
Cultivar FH-142 performed better than MNH-886 in terms of different yield components. There was a good
agreement between observed and simulated days to anthesis (0 to 1 day), days to maturity (0 to 2 days), seed
cotton yield, total dry matter and harvest index with an error of -4.4 to 15%, 12–7.5% and 13–9.5% in MNH886 and for FH-142, 4-16%, 19-11% and 16-8.3% for growing years 2013 and 2014, respectively. CROPGROCotton-P would be a useful tool to forecast cotton yield under different levels of P in cotton production system
of the semi-arid climate of Southern Punjab.
Key words: Crop modeling, DSSAT, Phosphorus, Environmental Protection, Cotton Cultivars
Introduction
2
Cotton is one of the most important exporting commodities of Pakistan which contributes up to 7.1% in
agriculture value additions (GOP 2015). Cotton-based industry including, textile provides enormous
employment whereas cotton seed also fulfills 50-60% edible oil necessities of the country (Mubeen et al. 2012;
Khaliq et al. 2012; GOP 2015). Poor soil fertility and improper fertilizer application significantly hamper the
crop yields (including cotton) in Pakistan (Mubeen et al. 2016; Nasim et al. 2015; Hammad et al. 2010a,b). Plant
nutritionists estimated lower yields in Pakistan due to poor nutritional status of the soil (Warraich et al. 2012:
Fahad et al. 2016a, c, d). In general cotton crop gave various responses to nitrogenous fertilizers in all categories
of soil, but its response to phosphatic fertilizer was inconsistent in most of the areas (Ndor et al. 2010; Fahad et
al. 2015; 2016b). P is a vital macronutrient (in addition to nitrogen and potassium) for energy storage and
transfer in plants, cell division and development of tissues (Glass et al. 1980). There is also necessity of P for
strong root and shoot growth which helps to overcome the effects of soil minerals deficiency to increase the
water use efficiency (Snyder 2009; Nasim et al. 2011). P deficient soil has negative and rapid effects on the
development and yield in a range of crops (Singh et al. 2013). Cotton response to P fertilizer at medium or even
low soil test phosphorus levels was inconsistent. In cotton, it has vital role during early developmental phase,
early leaf blooming, number of floral buds and bolls, biomass production and final yield of cotton crop (Bronson
et al. 2001; Usman et al. 2010; Wajid et al. 2010).
Soil tests conducted in Pakistan showed a wide spread deficiency of phosphorus (Ali and Ali, 2011). Most of the
soils in Pakistan are alkaline and calcareous in nature where a nationwide study showed that plant available P is
deficient on 90% of soils (< 10 mg P kg-1) (Memon et al. 2001). Arid land soils contain more clay particles and
this factor increase the P fixation, therefore a large amount of P must be applied in order to optimize P
availability to plants during their vegetative growth phase (Dzotsi et al. 2010). The recovery of P is reported to
be only 15-25% of the amount applied in irrigated areas whereas, it is not well-documented in arid areas which
is supposed to be further less than that of the irrigated areas (Chattha et al. 2007; Nasim et al. 2011, 2012,
2016a, b, c). Surveys in various cotton producing districts have shown that almost all cotton farmers apply
nitrogenous fertilizers; however, about 85% farmers use phosphatic fertilizers to increase their crop production
(Makhdum et al. 2001).
Unlike many developed agricultural countries, Pakistan is not up to the mark in terms of fertilizer scheduling,
nutrient recycling and optimization of fertilizer use efficiency. Thus, the quantitative optimization of important
nutrients like P through field-based studies and predictive modelling approaches in major crops like cotton is
need of the time. Understanding the P dynamics and its role in P nutrition of crops and agro-ecosystems can
3
been enhanced via mathematical modeling approaches (Thorp et al. 2014). Several studies have demonstrated
the use of models in evaluating the inorganic P dynamics (Janssen et al. 1987; Wolf et al. 1987; Tinker and Nye
2000). Such studies have been used to assess the recovery of P fertilizer in the year of application and its
residual effect in the following years. Other P models such as by Jones et al. (1984), a component of the Erosion
Productivity Impact Calculator (EPIC) crop growth model (McCown et al. 1996), focused on P adsorption
processes with the general aim of prediction of P losses in sediments (Dzotsi et al. 2010). Cropping System
Model (CSM) CROPGRO-Cotton under Decision Support System for Agro-technology Transfer (DSSAT) was
developed through a collaborative effort between scientists at the University of Florida and University of
Georgia (Thorp et al. 2014). The CSM model executes a simple computer code for simulation of the soil water,
inorganic soil N, and organic C and N balances (Ritchie et al. 2009). Model was used to deal with the expected
risks in the agricultural production (Nasim 2016b; Amin et al. 2015).
Recently a soil P application module was also added to CSM. In this module it was the first effort to simulate P
in the DSSAT (Jones et al. 2003; Dzotsi et al. 2010). So, CSM-CROPGRO-P can simulate the effects of P on
the cotton yield and yield components with great precision (Thorp et al. 2014). Irrigation, time of sowing and N
fertilizer simulation through DSSAT has been previously tested in arid and semi-arid conditions of Pakistan
(Nasim et al. 2012; Mubeen et al. 2013; Wajid et al. 2014); however, meager information is available on its use
for P modeling under diverse climatic conditions. To best of our knowledge, this is the first study conducted to
simulate P application in cotton grown under semi-arid conditions in southern Punjab, Pakistan by using CSMCROPGRO-Cotton-P so that the quantitative optimization of phosphorus may be performed through predictive
modeling technique in order to avoid over- (environmental concern) and under-use of phosphorus (nutritional
concern) in the cotton of the semi-arid region.
Materials and Methods
Experimental details
A two year field study was conducted during 2013 and 2014 at dry semi-arid region Vehari, Punjab (30.07°N,
72.32°E) to evaluate the phenology and yield of cotton in response to various phosphorus (P) levels. The
experimental treatments included two cotton cultivars (MNH-886 and FH-146); three P levels (control P= 0 kg
P ha-1; medium P = 57 kg P ha-1; high P = 114 kg P ha-1) were chosen based on the one lower and one upper
levels of recommended dose of P (57 kg ha-1) by Adaptive Research Farm, Vehari. Cotton crop was seeded on
April 15 and March 27 during 2013 and 2014, respectively with bed-furrow method by maintaining 75 cm inter-
4
row spacing at the seed rate of 25 kg ha-1. Before seeding the field was disked followed by cultivation, and
planking operations. Cotton cultivars were collected from Adaptive Research Farm, Vehari. The experiment was
laid out in a randomized complete block design (RCBD) with split plot arrangement and a net plot size of 7.5 m
×13.0 m. Cotton cultivars were placed in main plots whereas, P levels in sub plots and experiment was
replicated three times. Thinning was completed after crop emergence to maintain uniform plant-to-plant
distance of 45 cm with 30,000 plants ha-1. Nitrogen was applied in the form of urea in five splits, 50 kg ha -1 at
sowing, in first split and remaining in other four (emergence of first square; maturity of first flower; first open
boll; at maturity) splits (Makhdum et al. 2001). Phosphorus was applied at the time of sowing; the application
sequence of fertilizer shown in the Table 2. All other agronomic practices such as irrigation, weeding, plant
protection measures and earthing-up were carried out according to the standard recommended practices of the
cotton production area.
Soil physiochemical analysis
Composite soil samples from 0-15 cm and 15-30 cm soil depth were obtained with the help of soil augur from
the trial area prior to seeding of the cotton cultivars during both the study years. The convenient method for the
laboratory analysis of sedimentation was used to analyze the sand, silt and clay percentage in the soil samples
and international textural triangle procedure was used to find textural class. Bouyoucous hydrometer method
using one percent sodium hexameta phosphate as a dispersing agent was used to find out the percentage of sand
and clay (Beretta et al. 2014). Different physico-chemical properties of loam soil were analyzed by using the
method as described by Homer and Pratt (1961); soil parameters are shown in Table 1. Jaranwala soil series
profile was similar soil characteristics with study location and used for crop simulation modeling.
Weather data
Weather
data
were
obtained
for
the
experiment
site
from
NASA
weather
observatory
http://power.larc.nasa.gov/cgi-bin/cgiwrap for 2013 to 2014. Daily maximum and minimum temperature (oC),
precipitation (mm) and solar radiations (M J m-2 d-1) for both years were used to develop DSSAT weather file
(Fig. 1).
Crop growth data collection and modeling
Different coefficients were measured from crop during both years including anthesis date (ADAT),
physiological maturity (MDAT), emergence day (EDAP), number of days from planting to harvest (NDCH), lint
5
yield at maturity (LIWAM), yield at harvest maturity (HWAM), unit eight at maturity (HWUM), tops weight at
maturity (CWAM), harvest date (HDAT), harvest index (HIAM), panicle weight at maturity (PWAM) and
product produced at maturity (BWAM). The collected field data from the experiment during growing season
were used for calibration and validation of CROPGRO-Cotton-P model. Standard meteorological, soil, plant
characteristic and crop management data were obtained and used as input data for the model. Decision Support
System for Agro technology Transfer (DSSAT) was used for the estimation of crop genetic coefficients for
selecting the best treatment among the cotton cultivars.
Soil phosphorus module (CSM-CROPGRO-P)
Specifically, (1) the CENTURY model (Parton et al. 1988) was adapted for organic P dynamics and integrated
with the original version of the soil–plant P model by Daroub et al. (2003), and (2) regression equations linking
resin extractable P and other forms of soil extractable P (like Bray1 and Olsen methods) were derived from
Sharpley et al. (1984, 1989) to improve the initialization of inorganic and organic P pools. In addition, three
other important modifications were added to the model: (1) partitioning the inorganic P pools into two soil
volumes, a soil volume that is within a radius adjacent to roots and the remaining bulk soil in such a way that
only P present in the proximity of roots is taken up by the plant; (2) the rates of inorganic P transformation
depend on soil category (calcareous, slightly weathered and highly weathered) and soil properties and (3) the
equilibrium P concentration in the soil solution available for plant uptake is related to soil texture, soil water and
soil organic matter factors (Dzotsi et al. 2010).
So in DSSAT v4.6, the soil plant P model depends on two soil modules (organic and inorganic) and one plant
module (Hoogenboom et al. 2015). The main structure of the P model consisted of two basic sections: (i)
inorganic P, and (ii) organic P, with plant uptake which have relation with both sections. Inorganic P is readily
available by mineralization from the organic compartment. Now while discussing the inorganic P compartment,
P processes are further divided into the “Root” soil region (the soil volume currently explored by roots and is a
function of the root length density) and the “NoRoot” (where roots have no access) region. P transfers occur
between and within the compartments. Inorganic phosphorus is incorporated by the plant used phosphorus in
terms of dry matter (Dzotsi et al. 2010).
Moreover, three other important improvements were done to the DSSAT model by dividing the inorganic P
bands into two soil volumes, (1) P is available within the adjacent to root rhizosphere in the first soil volume and
the other remaining soil volume in which that only P present in the region of roots is uptake by the plant; (2)
6
Soil category (highly weathered, slightly weathered and calcareous) and properties of soil improve the inorganic
P availability rates to the plants; (3) the balance P availability in the soil for plant uptake is dependent on soil
organic matter, soil water and soil texture (for further details please see Dzotsi et al. 2010).
Model calibration and evaluation
Calibration is a process of adjusting some model parameters to our own climatic and growth conditions. It is
also necessary for getting genetic coefficients for new cultivars used in modeling study. Therefore, the
CROPGRO-Cotton-P was calibrated with data including crop phenology, above ground biomass production and
yield components collected during year 2013 against medium P level (57 kg ha-1), which performed best in
terms of yield and yield components for both cotton cultivars under field conditions in Vehari. Cultivar
coefficients successively started from CSDL (critical short day length) and PPSEN (slope of the relative
response) for the development of photoperiod with time to PODOUR (the time required for cultivar to reach
final pod load under optimal conditions). Eighteen coefficients control the crop phenology, growth and seed
cotton yield in CROPGRO-Cotton V-4.6.1 (Hoogenboom et al. 2015). These cultivars coefficients were used to
calibrate ADAT, MDAT, HWAM, HIAM and CWAM for this study.
For selecting the most suitable set of coefficients, an iterative approach was used. Calculated coefficients for
two cotton cultivars and their detailed descriptions are given in Table 4. To check the accuracy of the model
simulations, it was run with data recorded against control and high P application rates. During all this process,
the available observed data about crop phenology (anthesis and maturity date) and seed cotton yield was
compared with simulated values using same genetic coefficients. Simulation performance was evaluated by
calculating different statistical indices like root mean square error (RMSE) according to Hoogenboom et al.
(2015). For individual treatment, % error between simulated and observed values was calculated as below,
RMSE = [∑𝑛𝑖=1(P𝑖 − O𝑖)2 /n]0.5
Error (%) = (
(P−O)
O
)100
(1)
(2)
Where Pi is predicted and Oi is observed value for studied variables and n represents the number of observations
in equations 1 and 2.
7
Easy Grapher V-4.6.1 is a built-in graphic utility incorporated by DSSAT for statistical analysis of crop
simulation models such as CANB (Canadian Agricultural Nitrogen Budget Model). It enables users to validation
graphs for simulation, evaluation statistics such as root mean square error (RMSE), mean percent error (E),
forecasting efficiency (EF), degree of agreement (D) and validation of the Easy Grapher (Yang et al. 2014). The
consequent simulation outcomes are described as below where simulated (yi) and observed value (xi) given in
equations 3 to 5,
𝑬 = [∑(𝒚𝒊 − 𝒙𝒊)/𝒏]
̅ )𝟐
𝑬𝑭 = 𝟏 − ∑(𝒚𝒊 − 𝒙𝒊)𝟐 / ∑(𝒚𝒊 − 𝒚
̅| + |𝒙𝒊 − 𝒚
̅|)𝟐
𝑫 = 𝟏 − ∑(𝒚𝒊 − 𝒙𝒊)𝟐 / ∑(|𝒚𝒊 − 𝒚
(3)
(4)
(5)
The model calibration was performed with collected data (that included biomass and yield components) against
medium P treatment (57 kg P ha-1) in cotton cultivars (MNH-886 and FH-142) that performed best in field trials
in year 2013.
Results
Weather conditions
Figure 1 represents the mean monthly weather conditions for 2013 and 2014. Relatively less precipitation was
observed in March for 2013 which provide the dry condition at cotton sowing but high maximum temperature
was reported during this year. Precipitation is quite uneven during the month of June and August for 2013 (Fig.
1) which affected growth components (anthesis days and days to maturity) results decline in final yield as
compared to weather conditions of 2014. Maximum and minimum temperature decreased and high precipitation
was reported during September and October for 2014 improves the yield components (seed cotton yield, total
dry matter).
Calibration of the model
The CROPGRO-Cotton-P model gave good results in simulating final yield (seed cotton yield), crop phenology
(days to anthesis and maturity) and crop growth (seed cotton yield, total dry matter, harvest index) for
experimental site on the estimation of the cultivar coefficients. The model performed uniformly sound with the
similar set of genetic coefficients for crop phenology, crop growth and seed cotton yield.
8
Model simulations showed that crop reached anthesis stage 68-74 days after seeding in all treatments and the
observed days ranged from 68-74 days which indicated that model was fit and worked well under environmental
conditions of Vehari (Table 3). RMSE values indicated that model worked well for simulation under determined
set of cultivar coefficients. Anthesis date is very important factor, in our field trail, 68 to 74 days was observed
which is similar with the days simulated by the model for MNH-886 and FH-142, respectively. Days to maturity
were observed 165 and 173 days similar status with days simulated by the model for MNH-886 and FH-142,
respectively (Table 3). While discussing the results of calibration, seed cotton yield simulated 2.13 Mg ha-1
versus observed 2.23 Mg ha-1 with the error (-4.40%) for MNH-886 and for FH-142 simulated yield was 2.55
Mg ha-1 and observed yield was 2.45 Mg ha-1 shows a error (4.00%) as shown in Table 3. Total dry matter
showed 12% error between simulation MNH-886 (8.57 Mg ha-1) with observed (7.62 Mg ha-1) while FH-142
showed -19 % error between simulated and observed total dry matter (Table 3). For the validation anthesis
observed 66 days but simulated value was 67 days one day higher than the observed and it showed the error
about 1.52 % and RMSE value is 1 for the cultivar MNH-886. FH-142 showed the same observation but error is
1.43% and RMSE value is 1. After the anthesis role of maturity day is important in MNH-886. We observed 163
days against the 165 simulated days with the error 1.23% and RMSE is 2 (Table 3). For FH-142 observed
maturity days are 170 against the 171 simulated days which have error almost 0.59% and RMSE equal to 1.
Seed cotton yield of the cultivar MNH-886 shows 1.64 Mg ha-1 while simulated yield for this cultivar 1.89 Mg
ha-1 with RMSE 0.25 and error 15%. On the other hand FH-142 observed seed cotton yield 1.92 Mg ha-1 with
simulated yield 2.24 Mg ha-1 with RMSE 0.32 and error of almost 17% (Table 3). Cultivar MNH-886 observed
value of the total dry matter 7.57 Mg ha-1 and simulated yield 8.14 Mg ha-1 with RMSE equal to 0.57 and error is
almost 7.5%. While FH-142 observed total dry matter 7.64 Mg ha-1 versus simulated yield 8.48 Mg ha-1 with a
RMSE value 0.84 shows error about 11% (Table 3). Observed value of harvest index for MNH-886 is 0.21 and
simulated value is about 0.23 which shows RMSE value 0.02 and error for this cultivar is 9.5%. Cultivar FH142 showed different behavior, harvest index observed is 0.24 and simulated harvest index for this cultivar is
slightly higher near about 0.26 with error 0.02% (Table 3). All the discussed variables showed that FH-142
gives good and positive response compared to MNH-886 and performance of the FH-142 is good than MNH886.
Days to anthesis
9
Simulated anthesis days were 68 which were similar as observed for MNH-886 for control P but in case of high
P (114 kg P ha-1), simulation showed one day lower than the observed days to anthesis. FH-142 showed
different behavior, one day higher simulated than the observed for control P treatment but no change in case of
high P treatment. Fig. 2 describes that model simulated days to anthesis one day higher in case of different P
applications. At controlled P treatment, days taken to anthesis were delayed than crop getting P at high P
application (114 kg ha-1). Similarly the model predicted one day higher in anthesis than observed in both
cultivars. Furthermore the dotted lines show the trend of the treatment with respect to observed and simulated
values (Fig. 2-6). According to model simulations, crop reached anthesis stage 67-71 days after seeding in both
cultivars and the observed days ranged from 66-70 days which indicated that model was fit and worked well
under environmental conditions of Vehari. Efficacy of the CROPGRO-Cotton-P model shows that percent error
for individual treatment of both cultivars ranged from 0 to 2.9% for observed and simulated days to anthesis.
Fig. 2 shows fitness of the data between observed and simulated data for MNH-886 and FH-142. Similarly,
validation of model for cultivars shows that crop reached at anthesis stage in 67-71 days after sowing. The
observed values ranged from 66-70, closer to simulated which depicts the usefulness of model with independent
set of data. RMSE between the observed and simulated anthesis days (0.9-1.2), EF of the model (1.0-0.6),
goodness of the model for D had high value of (1.0-0.9), simulated and observed values was closer to 1:1 line
with a E (0-1), for years 2013 and 2014, respectively showed that model simulated the anthesis dates very well
as it was displayed in the field (Fig. 2).
Days to maturity
Data presented in Fig. 3 reveals that model simulated 3 days more to maturity than the observed ones in case of
MNH-886 for 2014 but it showed difference of one day for 2013. FH-142 cultivar matured in 171-173 days
whereas MNH-886 matured in 165-165 days according to model simulations for 2014 and 2013, respectively.
The observed values at maturity for FH-142 and MNH-886 were 171-174 and 165-165 days at high P treatment
(114 kg P ha-1) for years 2014 and 2013, respectively.
The two P levels showed simulation of three days higher than the observed in case of MNH-886 for 2014 and
one day more for 2013. Similarly FH-142 showed a difference of two days in simulated from observed at
control P treatment, but at high P rate model simulated identical values as observed for both years. Percentage
error ranged 0.6-1.9% with RMSE of 1-3 for MNH-886 control P and in case of FH-142 error was observed
0.6-1.2% for control P but 0.6-0% for high P treatments for 2013 and 2014, respectively. Model evaluation for
10
MNH-886 and FH-142 also remained satisfactory in which prediction remained 1-2 days and 1-3 day more than
the observation for years 2013 and 2014, respectively. The MNH-886 matured in 165-165 days versus FH-142
with 171-173 days according to model simulations with corresponding observed values of 164-162 days and
174-171 days for 2013 and 2014, respectively. The range of RMSE for MNH-886, FH142 was 0.7-2.1 and EF
between the simulated and observed days to maturity was found to be 1.0-0.7, E for maturity days 0.2-1.8
showing acceptable limit, Simulated and observed maturity dates were very close to 1.1 line for both the
cultivars having higher values of D 1.0-0.9 showed the goodness of the model during evaluation and validation
for 2013 and 2014, respectively.
Seed cotton yield
Figure 4 shows that MNH-886 cultivar observed seed cotton yield of 2.23-1.64 Mg ha-1 whereas FH-142 cultivar
produced 2.45-1.92 Mg ha-1 for 2013 and 2014, respectively according to model simulations the yield 2.13-1.89
Mg ha-1 and 2.55-2.24 Mg ha-1 at medium P level (57 kg ha-1) for 2013 and 2014, respectively. Percentage error
between the observed and simulated values for MNH-886 and FH-142 was significant. Evaluation of the model
for MNH-886 also remained satisfactory for high P application (with the value of RMSE 0.12-0.16 Mg ha-1) and
FH-142 at same P level (RMSE value was equal to 0.27-0.20 Mg ha-1) for 2013 and 2014, respectively. In case
of P levels, percentage error was almost same for both cultivars it was very consistent and almost equal to 10%
in the growing year 2014 but in 2013 percentage error was double for FH-142 then MNH-886. Fig. 4 illustrates
the scatter of simulated and observed seed cotton yield around the regression line. There was strong and positive
correlation between observed and simulated data such as RMSE 0.34–0.36, E 0.23–0.28, EF -1.15 to -0.92 and
D 0.58-0.60 for the growing years 2013 and 2014, respectively. Model predicted higher 0.28-0.26, 0.25-0.23
harvest index at high P rate (114 kg P ha-1) than at low rate of control P for FH-142 and MNH-886 in growing
year 2013 and 2014, respectively. Similarly when CROPGRO-Cotton-P model was validated with control P
error was observed and simulated harvest index was lower 19–4.5% than at high P (19-9.5%) in the case of
MNH-886 but for FH-142 error in harvest index was higher 12-4% at control P and 16-0% at high P application
(114 kg P ha-1) in growing year 2013 and 2014, respectively.
Total dry matter
The model overestimated total dry matter than the observed values indicating that there is a potential of
producing more total dry matter by cultivars MNH-886 and FH-142. The data in Fig. 5 indicated a significant
and positive correlation among simulated and observed total dry matter for cultivars. Data showed that model
11
slightly over estimated 6.7-1.3% total dry matter in MNH-886 than FH-142 which model estimation was 176.4% more than observed data for 114 kg P ha -1 for 2013 and 2014, respectively. RMSE for cultivars ranged
from 1.4 to 0.9 Mg ha-1 for MNH-886 and 0.5-0.1 Mg ha-1 for FH-142 at control P treatment between observed
and simulated data of total dry matter accumulation for year 2013 and 2014, respectively. Model validation with
independent set of data for both cultivars were also good with error of 19-13% and 21-26% good agreement for
MNH-886 and FH-142 to 2013 and 2014, respectively which showed that model was robust in validation under
various climatic conditions. EF between observed and simulated data for the both cultivars and P application
rates was estimated at -1.7 to -1.0 with E 0.79–0.71 and D 0.53–0.48 which shows a good agreement for the
simulated and observed total dry matter RMSE 0.8-0.7 Mg ha-1 and D was observed 0.53–0.48 for 2013 and
2014, respectively shown in Fig. 5.
Harvest index
Figure 6 shows the comparison between the simulated and observed data of the harvest index for 2013 and
2014. In case of harvest index percentage error in 2013 was higher than 2014 for both cultivars under different P
levels. The relationship of simulated and observed for harvest index was found to be linearly and positively
related (Fig. 6). The relation between the simulated and observed for different statistical variables such as
RMSE, E and EF observed 0.03-0.02, 0.03-0.02 and - 5.5 to -0.2 respectively D 0.5-0.7 for 2013 and 2014,
respectively which showed the close relation between the observed and simulated harvest index.
Discussion
CSM-CROPGRO-Cotton model was found to have the ability to evaluate the cotton performance for climate
change situations (Wajid et al. 2004, 2014). So it was pre-requisite to evaluate this model for quantification of
management options (different P applications for promising two cotton cultivars) under climatic conditions of
Vehari. Many scientists used CSM-CROPGRO-Cotton for the simulation of growth, development, and yield of
cotton for different weather and soil conditions and management practices (Jones et al. 2003; Hoogenboom et al.
2004; Ortiz et al. 2009; Pathak et al. 2009).
In our study, generally it showed decrease in RMSE values due to the weather variations as model performance
was measured by RMSE values (Soler et al. 2007). Climate variations showed the close RMSE values for 2014
than 2013 (Table 3). This determines the good model performance. All the treatments described by the CSMCROPGRO-P model simulation showed the identical behavior towards the climatic variations shown in Figure
12
1. Anthesis days, maturity days, total dry matter and harvest index were generally simulated with greater
precision for 2014 than 2013 as the climatic conditions were uneven and high precipitation was reported in
2013 (Fig. 1) as reported by Amin et al. (2016). The simulation was significantly improved for anthesis,
maturity days and harvest index when P was applied. While the seed cotton yield and total dry matter showed
greater variations with P application (Table 3). Simulation shows that phosphorous deficiency in cotton
cropping system is quite challenging issue.
RMSE values indicated that model worked well for simulation under determined set of cultivar coefficients. In
case of days to maturity, RMSE of 0 during calibration for 2013 and RMSE of 2-1 for 2014 and 1.2–0.6 was
found during evaluation for MNH-886 and FH-142, respectively. The crop which was applied at medium 57 kg
P ha-1 matured 9 days earlier during 2013, 6 days earlier in 2014 over treatment with high P application (114 kg
ha-1). In general, percentage error between simulated and observed days to maturity was more at higher levels of
phosphorous application (114 kg ha-1) than at medium P level (57 kg P ha-1) for MNH-886 and % error between
simulated and observed days to maturity was lower (0.6) in 2013 then 2014 (1.9). Li et al. (2009) concluded that
the simulated values of boll maturation period were very consistent with the observed values, with RMSE lower
than 3 days.
The model overestimated total dry matter than the observed values indicating that there is a potential of
producing more total dry matter under the current agro-ecological condition. Percent difference increased to 127.5% for MNH-886 and 19-11% for FH-142 in calibration at medium fertilizer rate (57 kg P ha -1) for 2013 and
2014, respectively. Model validation with control P treatment showed percent error of 21-26% for FH-142 and
19–13% for MNH-886 during growing years 2013 and 2014, respectively. But it showed lower percentage error
at higher P rate (114 Kg P ha-1) which was almost 6.7-1.3% for MNH-886 and 17-6.0% for FH-142 in 2013 and
2014, respectively. These results are similar with Ortiz et al. (2009), who reported that CROPGRO-Cotton
simulated biomass with error range of 6% to 18.4%. Zamora et al. (2009) found that the model provided a close
agreement between measured and simulated biomass both in 2001 and 2002.
The model simulated seed cotton yield reasonably well with error percentage of -4.4 - 15% having RMSE
values 0.09-0.31 Mg ha-1 for MNH-886 and 4-16% for FH-142 at the medium P during 2013 and 2014,
respectively. Similarly Ortiz et al (2009) estimated that prediction by CROPGRO-Cotton for seed cotton fell
within a range of -11.2% to 2.7%. As estimated by Wajid et al. (2014) model simulated seed cotton yield
reasonably well with error percentage of 3.2 to 7.1 during 2009 having RMSE values 0.13 to 0.22 Mg ha -1 in all
13
the treatments of sowing date, nitrogen levels and cultivars. Similarly during 2010, error %age in the prediction
of seed cotton yield was in the range of 1.6 to 6.4 with RMSE of 0.12 to 0.17 kg ha -1. Cammarano et al. (2011)
found that lint yield was well simulated by CSM-CROPGRO-cotton model for all the treatments (RMSE 0.10
Mg ha-1 for the 2008 growing seasons and 0.22 Mg ha -1 for the 2009 growing season). When the model was run
in different locations between south east Queensland and northern New South Wales it accurately simulated
cotton. Model simulation for harvest index shows very close relation by the CSM-CROPGRO-P between
observed and simulated values in case of 2014 lower than 2013 harvest index simulation. Highest percentage
error between observed and simulated for MNH-886 and FH-142 were 19–9.5% and 16– 0% for 2013-2014,
respectively.
Conclusion
The results showed good agreement between observed and stimulated values of total dry matter, harvest index
and seed cotton yield with an error of 12–7.5 %, 13–9.5% and -4.4 to 15% in MNH-886 and for FH-142 error of
19-11%, 16–8.3% and 4-16%, for growing years 2013 and 2014, respectively. MNH-886 and FH-142 at the rate
of 57 kg P ha-1 gave good results for the both years but showed better yield in 2013 growing year. Phosphorus
model is just developed for simulating effects of phosphorus on yield of different crops. CSM-CROPGROCotton-P model of DSSAT V-4.6 performed well under the climatic conditions of Vehari for various cultivars
and phosphorus levels. The results of present study not only have practical P-use recommendations for cotton
growers but also demonstrate the pragmatic implications of crop growth modeling in modern day crop
production.
Acknowledgment
The first author is grateful to the International Global Change Institute (IGCI) Hamilton, New Zealand, for
providing the software (SimCLIM 2013) and the required climatic dataset for future projections with for
southern Punjab, Pakistan. The first author is thankful to Prof. Dr. Gerrit Hoogenboom (Ex-Director,
AgWeatherNet, Washington State University, USA; Currently: University of Florida-USA), for his technical
guidance and support during the entire period of study and modeling work. The corresponding author (Wajid
NASIM) is highly thankful to Government of Australia, for Endeavour Research Award/Fellowship (No.
4915_2015) to The Commonwealth Scientific and Industrial Research Organization (CSIRO), Sustainable
Agriculture, National Research Flagship, Toowoomba-QLD 4350, Australia. Furthermore, co-authors (Wajid
NASIM and Shakeel AHMAD) are highly thankful for Higher Education Commission (HEC) of Pakistan for
partial funding.
References
14
Ali L, Ali M (2011) Relationship of yield and yield components of cotton (Gossypium hirsutum L.) with P and
K fertilization. J Agric Res 49:1117
Amin A, Mubeen M, Hammad HM, Nasim W (2015) Climate Smart Agriculture-a solution for sustainable
future. Agric Res Commun 2(3):13–21
Amin A, Nasim W, Mubeen M et al. (2016) Regional climate assessment of precipitation and temperature in
Southern Punjab (Pakistan) using SimCLIM climate model for different temporal scales. Theor Appl
Climatol. doi:10.1007/s00704-016-1960-1.
Beretta AN, Silbermann AV, Paladino L, Torres D, Bassahun D, Musselli R, García-Lamohte A (2014) Soil
texture analyses using a hydrometer: modification of the Bouyoucos method. Ciencia e Investigación
Agraria 41(2): 263–271
Bronson KF, Onken AB, Booker JD, Lascano RJ, Provin TL, Torbert HA (2001) Irrigated cotton lint yields as
affected by phosphorus fertilizer and landscape position. Communications in soil science and plant
analysis 32:11–12
Cammarano D, Payero J, Grace P, Basso B (2011) Evaluating the CROPGRO-Cotton Model for Estimating
Cotton Production. Presented in: International Annual Meetings 16-19 Oct. 2011
Chattha TH, Yousaf M, Javeed S (2007) Phosphorus adsorption as described by Freund lich adsorption
isotherms under rainfed conditions of Pakistan. Pak J Agri Sci 4(44):551–556
Daroub SH, Gerakis A, Ritchie JT, Friesen DK, Ryan J (2003) Development of a soil–plant phosphorus
simulation model for calcareous and weathered tropical soils. Agric Syst 76:1157–1181
Dzotsi KA, Jones JW, Adiku SGK, Naab JB, Singh U, Porter CH, Gijsman AJ (2010) Modeling soil and plant
phosphorus within DSSAT. Ecol Model 221:2839–2849
Fahad S, Hussain S, Saud S, Hassan S, Chauhan BS, Khan F, et al. (2016c) Responses of Rapid Viscoanalyzer
Profile and Other Rice Grain Qualities to Exogenously Applied Plant Growth Regulators under High
Day and High Night Temperatures. PLoS ONE 11(7): e0159590. doi:10.1371/journal.pone.0159590
Fahad S, Hussain S, Saud S, Hassan S, Ihsan Z, Shah AN, Wu C, Yousaf M, Nasim W, Alharby H, Alghabari F
and Huang J (2016d) Exogenously Applied Plant Growth Regulators Enhance the MorphoPhysiological Growth and Yield of Rice under High Temperature. Front. Plant Sci. 7:1250. doi:
10.3389/fpls.2016.01250
Fahad S, Hussain S, Saud S, Hassan S, Tanveer M, Ihsan MZ, Shah AN, Ullah A, Nasrullah, Khan F, Ullah S,
Alharby H, Nasim W, Wu C, Huang J (2016b) A combined application of biochar and phosphorus
alleviates heat-induced adversities on physiological, agronomical and quality attributes of rice. Plant
Physiol Biochem 103:191–198
Fahad S, Hussain S, Saud S, Khan F, Hassan S, Amanullah Jr, Nasim W, Arif M, Wang F, Huang J (2016a)
Exogenously Applied Plant Growth Regulators Affect Heat-stressed Rice Pollens. J Agron Crop Sci. doi
202:139–150
15
Fahad S, Hussain S, Saud S, Tanveer M, Bajwa AA, Hassan S, Shah AN, Ullah A, Wu C, Khan FA, Shah F,
Ullah S, Chen Y, Huang J (2015) A biochar application protects rice pollen from high-temperature
stress. Plant Physiol Biochem 96:281–287
Glass ADM, Beaton JD, Bomke A (1980) Role of P in plant nutrition. In Proceedings of the Western Canada
Phosphate Symposium, pp, 357–368
Government of Pakistan (2015) Pakistan Ecnomic survey 2014-2015. Pakistan Economic survey from
http://www.finance.gov.pk/survey/chapters_13/02-Agriculture.pdf.
Hammad HM, Ahmad A, Azhar F, Wajid SA, Khaliq T, Nasim W, Farhad W (2010a) Optimizing water and
nitrogen requirement in maize under semi arid conditions of Pakistan. Pak J Bot 43 (6):2919–2923
Hammad HM, Ahmad A, Laghari KQ, Abbas F, Nasim W, Farhad W, Malik AH ( 2010b) Organic farming in
wheat crop under arid conditions of Pakistan. Pak J Agri Sci 48(2):97–102
Homer DC, Partt PF (1961) Methods of analysis of soils, plants and waters. Univ. of California, Div Agri Sci
USA, pp, 150–196
Hoogenboom G, Jones JW, Wilkens PW, Porter CH, Batchelor WD, Hunt LA, Gijsman AJ (2004) Decision
support system for agrotechnology transfer version 4.0. University of Hawaii Honolulu HI (CD–ROM)
Hoogenboom G, Jones JW, Wilkens PW, Porter CH, Boote KJ, Hunt LA, Singh U, Lizaso JL, White JW,
Uryasev O, Ogoshi R, Koo J, shelia V, Tsuji GY (2015) Decision Support System for Agrotechnology
Transfer (DSSAT) Version 4.6.1.0 (www.DSSAT.net) DSSAT Foundation, Prosser, Washington,USA
Janssen BH, Lathwell DJ, Wolf J (1987) Modeling long-term crop response to fertilizer phosphorus. II.
Comparison with field results. Agron J 79:452–458
Jones CA, Cole CV, Sharpley AN, Williams JR (1984) A simplified soil and plant phosphorus model. I.
Documentation. Soil Sci Soc Am J 48:800–805
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt A, Wilkens PW, Singh U, Gijsman AJ,
Ritchie JT (2003) DSSAT Cropping System Model. Eur J Agron 18:235–265
Khaliq T, Mubeen M, Ali A, Ahmad A, Wajid A, Rasul F, Nasim W (2012) Effect of Diverse Irrigation
Regimes on Growth Parameters and Yield of Cotton under Faisalabad Conditions. IPJST 2(3):81–85
Li W, Zhou Z, Meng Y, Xu N, Fok M (2009) Modeling boll maturation period, seed growth, protein, and oil
content of cotton (Gossypium hirsutum L.) in China. Field Crops Res112:131–140
Makhdum MI, Nawaz MAM, Shabab-ud-Din, Chaudhry FI (2001) Effect of Phosphorus Fertilizer on Growth,
Yield and Fibre Quality of Two Cotton Cultivars. J res Sci 12(2):140–146
McCown RL, Hammer GL, Hargreaves JNG, Holzworth DP, Freebairn DM (1996) APSIM: a novel software
system for model development, model testing and simulation in agricultural systems research.
Agricultural systems 50(3):255–271
16
Memon KS, Rashid A (2001) Soil and fertilizer phosphorus. Book Soil Science pp, 291–316
Mubeen M, Ahmad A, Khaliq T, Hammad HM, Sultana SR, Ahmad S, Nasim W (2016) Application of CSMCERES-Maize model in optimizing irrigated conditions. Outlook on Agri 45(3):01–12
Mubeen M, Ahmad A, Wajid A, Khaliq T, Bakhsh A (2013) Evaluating CSM-CERES-maize model for
irrigation scheduling in semi-arid conditions of Punjab, Pakistan. Int J Agri Bio 15(1):1–10
Mubeen M, Khaliq T, Ahmad A, Ali A, Rasul F, Hussain J (2012) Quantification of Seed cotton yield and water
use efficiency of cotton under variable irrigation schedules. Crop Environ 3:54–57
Nasim W, Ahmad A, Bano A, Olatinwo R, Usman M, Khaliq T, Wajid A, Hammad H, Mubeen M, Hussain M
(2012) Effect of Nitrogen on Yield and Oil Quality of Sunflower (Helianthus Annuus L.) Hybrids under
Sub Humid Conditions of Pakistan. American J Plant Sci 3:243–251. doi:10.4236/ajps.2012.32029.
Nasim W, Ahmad A, Belhouchette H, Hoogenboom G (2016a) Evaluation of the OILCROP-SUN model for
sunflower hybrids under different agro-meteorological conditions of Punjab-Pakistan. Field Crops Res
188:17–30
Nasim W, Ahmad A, Wajid A, Akhtar J, Muhammad D (2011) Nitrogen effects on growth and development of
sunflower hybrids under agro-climatic conditions of Multan. Pak J Bot 43(4):2083–2092
Nasim W, Belhouchette H, Ahmad A, Rahman MH, Jabran K, Ullah K, Fahad S, Shakeel M, Hoogenboom G
(2016b) Modelling climate change impacts and adaptation strategies for sunflower in Punjab-Pakistan.
Outlook on Agri 45(1):39–45
Nasim W, Belhouchette H, Tariq M, Fahad S, Hammad HM, Mubeen M, Abbas T (2015) Correlation studies on
nitrogen for sunflower crop across the agroclimatic variability. Environ Sci Pollut Res 23(4):3658–3670
Nasim W, Belhouchette H, Tariq M, Fahad S, Hammad HM, Mubeen M, Abbas T (2016c) Correlation studies
on nitrogen for sunflower crop across the agro climatic variability. . Environ Sci Pollut Res 23(4): 3658–
3670
Ndor E, Agbede O, Dauda S (2010) Growth and Yield Response of Cotton (Gossypiumspp) to varying Levels of
Nitrogen and Phosphorus Fertilization in Southern Guinea Savanna Zone, Nigeria. PAT 6:119–125
Ortiz BV, Hoogenboom G, Vellidis G, Boote K, Davis RF, Perry C (2009) Adapting the CROPGRO-Cotton
model to simulate cotton biomass and yield under southern root-knot nematode parasitism. Transactions
of the ASABE 52(6):2129–2140
Parton WJ, Stewart JWB, Cole CV (1988) Dynamics of C, N, P and S in grassland soils: a model.
Biogeochemistry 5:109–131
Pathak TB, Jones JW, Fraisse C, Wright D, Hoogenboom G, Judge J (2009) Uncertainty Analysis of
CROPGRO-Cotton Model. In AGU Fall Meeting Abstracts pp, 775
17
Ritchie JT, Porter CH, Judge J, Jones JW, Suleiman AA (2009) Extension of an existing model for soil water
evaporation and redistribution under high water content conditions. Soil Science Society of America
Journal 73(3):792–801
Sharpley AN, Jones CA, Gray C, Cole CV (1984) A simplified soil and plant phosphorus model. II. Prediction
of labile, organic, and sorbed phosphorus Soil Sci Soc Am J 48:805–809
Sharpley AN, Singh U, Uehara G, Kimble J (1989) Modeling soil and plant phosphorus dynamics in calcareous
and highly weathered soils. Soil Sci Soc Am J 53:153–158
Singh SK, Badgujar GB, Reddy VR, Fleisher DHT, imlin DJ (2013) Effect of phosphorus nutrition on growth
and physiology of cotton under ambient and elevated carbon dioxide. J Agron and Crop Sci 199:436–448
Snyder CS, Stewart WM (2009) Phosphorus Nutrition of Cotton. Potash& Phosphate Institute (PPI) 655
Engineering Drive, Suite 110 Norcross, GA 30092–2837, (www.ipni.net)
Soler CMT, Sentelhas PC, Hoogenboom G (2007) Application of the CSM-CERES-Maize model for planting
date evaluation and yield forecasting for maize grown off-season in a subtropical environment. Europ J
Agron 27(2):165–177
Thorp K, Ale S, Bange M, Barnes E, Hoogenboom G, Lascano R, Rajan N (2014) Development and
Application of Process-based Simulation Models for Cotton Production: A Review of Past, Present, and
Future Directions. J Cotton Sci 18:10–47
Tinker PB, Nye PH (2000) Solute movement in the rhizosphere. Oxford University Press. Ney York
Usman M, Arshad M, Ahmad A, Ahmad N, Zia-ul-Haq M, Wajid A, Khaliq T, Nasim W, Ali H, Ahmad S
(2010) Lower and upper baselines for crop water stress index and yield of Gossypium hirsutum L. under
variable irrigation regimes in irrigated semiarid environment. Pak J Bot 42(4):2541–2550
Wajid A, Ahmad A, Hussain M, Rahman MH, Khaliq T, Mubeen M, Rasul F, Usman Bashir U, Awais M, Iqbal
J, Sultana SF, Hoogenboom G (2014) Modeling growth, development and seed-cotton yield for varying
nitrogen increments and planting dates using DSSAT. Pak J Agri Sci 51:641–649
Wajid A, Ahmad A, Khaliq T, Alam S, Hussain A, Hussain K, Nasim W, Usman M, Ahmad S (2010)
Quantification of growth, yield and radiation use efficiency of promising cotton cultivars at varying
nitrogen levels. Pak J Bot 42(3):1703–1711
Waraich EA, Ahmad R, Halim A, Aziz T (2012) Alleviation of temperature stress by nutrient management in
crop plants: a review. J soil sci and plant nut 12(2):221–244
Wolf J, De Wit CT, Janssen BH, Lathwell DJ (1987) Modeling long-term crop response to fertilizer phosphorus.
I. The model. Agron J 79(3):445–451
18
Yang JY, Drury CF, Yang JM, Li ZT, Hoogenboom G (2014) EasyGrapher: Software for Data Visualization
and Statistical Evaluation of DSSAT Cropping System Model and the CANB Model. Int J Comput
Theory and Engineering 6(3):210–214
Zamora DS, Jose S, Jones JW, Cropper WP (2009) Modeling cotton production response to shading in a pecan
alley cropping system using CROPGRO. Agroforestry Sys 76:423–435
19
Table 1 Summary of field attributes and soil physicochemical characteristics
Physico-chemical properties of soil in experimental area
Plant
Soil depth
Soil EC
Year
Soil pH
(Sm-1)
(cm)
Plant available
Saturation
K (ppm)
percentage
available P
(ppm)
2013
1.70
8.3
9.0
167.66
35
2014
1.76
8.7
8.8
175.06
37
2013
1.62
8.5
4.2
101.66
35
2014
1.64
8.5
4.0
105.34
37
0-15
15-30
Table 2 Fertilizer applications to cotton cultivars in growing seasons during both study years
Number of fertilizer
Application time
Application
Cotton growth
Fertilizers rate
applications
(2013)
time(2014)
stages
(kg ha-1)
1
March 15, 2013
March 27, 2014
Sowing
*Treatments
2
April 12, 2013
April 23, 2014
Emergence of
N = 50
first square
3
May 9, 2013
May 20, 2014
Maturity of first
N = 50; P = 92
flower
4
June 19, 2013
June 26, 2014
First open boll
N = 50
5
July 20, 2013
July 12, 2014
At maturity
N = 50
*Nitrogen= 50 kg ha-1, control (no P application); medium P = 57 Kg ha-1, high P = 114 Kg ha-1
20
Table 3 Model Calibration for two cotton cultivars for the different yield variables with medium P application (57 kg P ha-1) and validation for year 2013 and 2014.
2013
Variables
2014
Cultivars
Observed
Simulated
%Error
RMSE
Observed
Simulated
%Error
RMSE
MNH-886
68
68
0
0
66
67
1.5
1
FH-142
74
74
0
0
70
71
1.4
1
MNH-886
165
165
0
0
163
165
1.2
2
FH-142
173
173
0
0
170
171
0.59
1
MNH-886
2.23
2.13
-4.40
0.09
1.64
1.90
15
0.25
FH-142
2.45
2.55
4.00
0.09
1.92
2.24
17
0.32
MNH-886
7.62
8.57
12
0.95
7.57
8.15
7.0
0.57
FH-142
7.71
9.18
19
1.47
7.64
8.49
11
0.84
MNH-886
0.22
0.25
13
0.03
0.21
0.23
9.5
0.02
FH-142
0.24
0.28
16
0.04
0.24
0.26
8.3
0.02
Anthesis (day)
Maturity (day)
Seed cotton yield (Mg ha-1)
Total dry matter (Mg ha-1)
Harvest index (%)
21
50
50
2014
45
45
40
40
Temperature (oC)
35
30
25
20
35
30
25
2013
20
15
15
Maximum Temperature
Minimum Temperature
10
Maximum Temperature
Minimum Temperature
10
5
5
May
June
July
August Sep
March April
Oct
May
June
Months
30
60
28
55
2013
55
50
26
35
22
30
20
25
18
20
2
16
15
14
10
5
0
July
Months
August Sep
30
2014
28
26
45
Precipitation (mm)
Precipitation (mm)
Solar Radiation (MJ/m /d)
24
40
June
Oct
50
45
May
August Sep
Months
60
March April
July
40
August
24
September
35
22
30
20
25
18
20
16
15
14
10
12
5
10
0
12
10
March April
Oct
Precipitation (mm)
May
2
Solar Radiation (MJ/m /d)
June
July
Months
Fig. 1 Weather conditions during experimental duration of crop in Vehari-Punjab-Pakistan.
22
August Sep
Oct
2
March April
Solar Radiation (MJ/m /d)
Temperature (oC)
2013
80
RMSE = 0.58
E
= 0.0
EF
= 0.95
D
= 0.99
Observed
75
2014
RMSE = 1.15
E
= 1.0
EF
= 0.56
D
= 0.91
2013
70
65
60
60
65
70
75
80
Simulated
60
Treatments
65
70
75
80
Simulated
*RMSE:Root mean square error; E:Mean error; EF:Forecasting efficiency; D:Degree of agreement
Fig. 2 Relationship between simulated and observed values of days to anthesis (days after seeding) in two cotton
cultivars grown with three different P levels.
180
RMSE = 0.71
E
= 0.17
EF
= 0.97
D
= 0.99
175
2013
RMSE = 2.12
E
= 1.83
EF
= 0.70
D
= 0.91
2014
Observed
170
165
160
155
150
150
155
160
165
170
175
180
150
155
160
165
170
Treatments
Simulated
Simulated
*RMSE:Root mean square error; E:Mean error; EF:Forecasting efficiency; D:Degree of agreement
175
180
Fig. 3 Relationship between simulated and observed values of days to maturity (days after seeding) in two
cotton cultivars grown with three different P levels.
23
3000
2013
2014
2700
Observed
2400
2100
1800
1500
1200
1200
1500
1800 2100
Simulated
2400
2700
3000
1200
Treatments
1500
1800 2100 2400 2700
Simulated
*RMSE:Root mean square error; E:Mean error; EF:Forecasting efficiency; D:Degree of agreement
3000
Fig. 4 Relationship between simulated and observed values of seed cotton yield (Mg ha -1) in cotton cultivars
grown with three different P levels.
10000
2014
2013
9500
Observed
9000
8500
8000
7500
7000
6500
6500
7000
7500
8000 8500
Simulated
9000
9500 10000
6000 6500 7000 7500 8000 8500 9000 9500 10000
Simulated
Treatments
*RMSE:Root mean square error; E:Mean error; EF:Forecasting efficiency; D:Degree of agreement
Fig. 5 Relationship between simulated and observed values of total dry matter production (Mg ha -1) in two
cotton cultivars grown with three different P levels
24
1.0
Observed
0.8
2013
RMSE = 0.03
E
= 0.03
EF
= -5.5
D
= 0.52
2014
RMSE = 0.02
E
= 0.02
EF
= -0.19
D
= 0.73
0.6
0.4
0.2
0.0
0.0
0.2
0.4
0.6
Simulated
0.8
1.0
0.0
Treatments
0.2
0.4
0.6
Simulated
0.8
1.0
*RMSE:Root mean square error; E:Mean error; EF:Forecasting efficiency; D:Degree of agreement
Fig. 6 Relationship between simulated and observed values of harvest index (%) in two cotton cultivars grown
with three different P levels.
25
Table 4 Calculated genetic cotton cultivars coefficients used in CSM-CROPGRO-Cotton-P model.
ECO#
CO0001
CO0003
CS
PPS
EM
FL
FL
SD
FL
LF
SL
SI
XF
WT
SF
SD
PO
THR
SD
SD
DL
EN
-FL
-SH
-SD
-PM
-LF
MAX
AVR
ZLF
RT
PSD
DUR
PDV
DUR
SH
PRO
LIP
1
23
23
2
0.01
0.01
3
51.5
54.2
4
13.5
14
5
18
20
6
55
55
7
117.8
117.8
8
0.75
0.74
9
185
185
10
180
180
11
1.3
1.3
12
0.18
0.18
13
27.5
27.5
14
27
27
15
2.9
2.9
16
79
79
17
0.153
0.153
18
0.12
0.12
VRNAME
MNH-886
FH-142
Abbreviations
Genetic coefficients description
Units
ECO#
Ecotype code to which this cultivar belongs
CSDL
Critical Short Day Length under which reproductive growth progress with no day duration cause(for short day plants)
hour
PPSEN
Slope of the comparative reaction of growth to photoperiod by(positive for shortday plants)
1/hour
EM-FL
Time among plant appearance and flower emergence (R1)
Photothermaldays
FL-SH
Time among first flower and first pod (R3)
Photothermal days
FL-SD
Time among first flower and first seed (R5)
Photothermal days
SD-PM
Time among first seed (R5) and physiological maturity (R7)
Photothermal days
FL-LF
Time among first flower (R1) and end of leaf extension
Photothermal days
LFMAX
greatest leaf photosynthesis speed at 30 C
350 vpm CO2
SLAVR
precise leaf area of cultivar under average growth situation
cm2g-1
SIZLF
Maximum size of full leaf (three leaflets)
cm2
XFRT
Maximum division of daily development that is partitioned to seed + shell
WTPSD
Maximum weight per seed
g
SFDUR
Seed filling interval for pod cohort at standard growth situation
Photothermal days
SDPDV
standard seed per pod under standard growing situation
Number pod-1
PODUR
Time required for cultivar to reach final pod load under optimal conditions
photo thermal days
THRSH
Threshing percentage. The maximum ratio of (seed/(seed+shell)) at maturity. Causes seeds to stop growing as their dry
weight increases until the shells are filled in a cohort
26