Abstract

Water is the most important limiting factor of cotton (Gossypium hirsutum L.) and wheat (Triticum aestivum L.) cropping systems in semi-arid conditions of Southern Punjab. A two-year field experiment (comprising of cotton-wheat cropping) was conducted in Vehari (Southern Punjab) to calibrate and validate a DSSAT model in the climatic conditions of 1 × CO2 concentration (conc.) (current). The model simulation during calibration was good with errors up to 4.7, 4.4, 10.1, 6.4 and −5.4% for days to anthesis, days to maturity, total dry matter, yield and HI, respectively for the cotton-wheat cropping system. During model validation, the error percentages were also under reasonable limits. So, the model was run under 2 × CO2 conc. (future) conditions and it showed a difference of −7.3 to 19.7% anthesis days, maturity days, total dry matter, grain yield, crop ET and WUEGY with respect to current CO2 concentration. Simulation by DSSAT showed that the cotton cultivar MNH-886 and wheat cultivar Lassani-2008 were better utilizers of limited water resources under changed climatic conditions in semi-arid conditions of Vehari, which was due to their better water use efficiency. Wheat and cotton cultivars with high water use efficiency would enable crop growth to maintain high crop yields under increased CO2 and its associated consequences in future.

INTRODUCTION

The cotton-wheat cropping system has prime significance for the agriculture-based Pakistan economy. This system not only assures food security to a large population in the country, but is also a major source of foreign exchange earnings. Cotton plays a major part in agriculture value addition (5.5%) and GDP (1.0%); it provides vital raw material to the textile industry, so it is indirectly a large source of foreign exchange. Cotton is also the highest edible oil producing crop in the country (GOP 2018). Owing to its contracted range of environmental adaptability, the cotton plant is very much influenced by the climate and various agronomic factors, such as genotypes, sowing time, and nitrogen and phosphorus fertilizers (Wajid et al. 2014; Amin et al. 2017). Wheat is the primary food grain of Pakistan and contributes in agriculture value addition (9.1%) and GDP (1.7%) (GOP 2018). A significant correlation has been found between the atmospheric CO2 amount and drought stress for the final wheat grain yield; raised CO2 level increased 10% yield in well-watered plots and 20% increase in the stressed plots. Water shortage at critical growth stages may have a great influence on wheat grain yield compared to similar stress during other stages; it was found that wheat cultivars also differ in their sensitivity to water stress (Eitzinger et al. 2003; Mubeen et al. 2013c; Nawaz et al. 2015). Because of many factors, for example, changed length of the developing season, wheat may be influenced absolutely or adversely in various regions of Pakistan. Adjustments, for example changed sowing dates, more effective utilization of water and more prominent innovative work to get maximum yield, crop variants may counterbalance the adverse effects of environmental change (Yu et al. 2013; Ahmad et al. 2015; Gorst et al. 2015).

The anthropogenic rise in greenhouse concentrations is causing significant anomalies in the climate at global and regional scales (IPCC 2014). Global circulation model predictions have warned about the frequency and severity of these changes in the future (Nasim et al. 2016a) and their profound biological, societal, and environmental impacts (Hautier et al. 2015; Hertel 2016; Nasim et al. 2016b; Amin et al. 2018a). Atmospheric CO2 is gradually expanding and is expected to rise from the present level of ∼384 to ∼550 μL L−1 by 2050. It is anticipated that worldwide temperatures will increment by 1.5–4.5 °C and additionally warm waves and dry season spells will be increased (Benlloch-Gonzalez et al. 2014). Agricultural crop production could be significantly affected by a change in atmospheric CO2 concentration and the accompanying climate change due to differences in photosynthesis, crop respiration, water use efficiency (WUE), as well as soil biological and chemical transformations of C and N (Guo et al. 2010; Wang et al. 2014; Long et al. 2015; He et al. 2018). If we examine the concept of water use by the cotton plant, there is a difference among the processes that occur at the leaf level compared to the canopy level. At the leaf level, water use is controlled by the available energy impinging on the leaf, vapor pressure deficit, and aerodynamic exchange, but it is regulated by stomatal conductance (gs). At the canopy level, the processes involve energy exchange at the soil surface and the plant canopy and water loss is a combination of evaporation from the soil surface and transpiration from the plant canopy. The combination of evaporation and transpiration is referred to as evapotranspiration (ET) and in the literature on WUE of plants, there is extensive usage of crop water use for determining WUE (Hatfield & Dold 2019). A variation in climatic conditions can change the water cycle due to a change in precipitation, the timing and magnitude of run-off, ET and also by any variation in the frequency and intensity of floods and droughts. Global warming and climate change are upsetting the water availability during critical growth stages and unsatisfactory institutional and physical infrastructure in the country is not suitable to cope with its negative impacts (Amin et al. 2018a).

Plentiful research has been conducted on the effects of climatic variability and change on field crop production as a function of water balance. Crop simulation models can logically approximate and quantify the impact of particular water stress on field crop production after calibration and validation in field experiments (Grossman-Clarke et al. 2001; Sharif et al. 2015). Worldwide, crop models are being extensively used by researchers and policy makers as important decision making tools for studying the effects of climate change and management practices including irrigation strategies on crop yields. Field experiments in these research areas are resource-intensive and challenging to implement. Under these circumstances, calibrated and validated crop models offer alternative solutions with comparable outcomes (Thorp et al. 2014; Modala et al. 2015). The modular structure of Decision Support System for Agro-technology Transfer (DSSAT) includes weather, soil, soil–plant–atmosphere interface, and management modules which drive the cropping system model plant growth module. Detailed information on the background and functions of DSSAT can be obtained in previous publications (Soler et al. 2007; Liu et al. 2011; He et al. 2012; Banger et al. 2018). The Cropping System Model (CSM)-CROPGRO-Cotton model is part of the group of crop simulation models which is included in the DSSAT (Hoogenboom et al. 2004; Amin et al. 2017). This model can simulate development, growth, and yield of cotton for diverse soil, weather and agronomic management (Ortiz et al. 2009; Amin et al. 2018c). CROPGRO is one of the first suites that revised weather simulation generators or presented a package to assess the model performance under climate change (Murthy 2004). The CERES (Crop Environment REsource Synthesis) model simulations have been tested under a wide range of climate conditions (Basso et al. 2016): monsoonal (Liu et al. 2013), semiarid (Mubeen et al. 2013a), Mediterranean (Hasegawa et al. 2000), oceanic and continental (Johnen et al. 2012), cold winter, and humid temperate (Otegui et al. 1996). CERES-Wheat showed valuable results during evaluating drought effects in some experiments. Jamieson et al. (1998), in their comparison of models AFRCWHEAT2, CERES-Wheat, SUCROS2, Sirius and SWHEAT with observations from wheat grown under drought at specific locations, found that the uncertainty in yield prediction by CERES-Wheat was less than 10%. CERES-Wheat has been successfully evaluated in numerous studies (Bannayan et al. 2003; Boote et al. 2008), particularly in studies of effects of climate change on growth (Eitzinger et al. 2003; Luo et al. 2003; Anwar et al. 2007; Kang et al. 2009; Nasim et al. 2012; Basso et al. 2016).

The DSSAT-CSM (Cropping System Model) can suitably study the climate change impact on the soil water needs of a cropping system, for example a wheat-cotton cropping system, due to the following reasons: the Land Unit and Primary modules of DSSAT-CSM link to sub-modules, and therefore may be used to aggregate information and processes pertinent to successive components of the cropping system. The soil in the land unit module is described as a one-dimensional profile; this is homogenous horizontally and comprises of a number of vertical soil layers. The DSSAT-CSM integrates models of all crops within one set of code letting all crops apply identical soil model components. This designed feature can significantly simplify the simulation for crop rotations as soil processes function continuously, and diverse crops are planted, managed, and harvested according to cropping system information given as inputs to the model. The weather module in DSSAT can change weather variables on a daily basis for studying climate change or simulating those experiments in which maximum and minimum temperatures, solar radiation, day length, rainfall, and/or atmospheric CO2 concentrations were fixed at some constant values, and increased or decreased with reference to their read-in values (Jones et al. 2003). One of the modes of operation in the model simulates crops over several weather years by using the same initial soil conditions. This mode can evaluate the impacts of uncertain future weather on decision making if we know all the initial soil conditions. Another mode of the model operates the cropping system modules for simulating crop rotations over many years, and the soil conditions are fixed only at the very beginning of the simulation (Jones et al. 2003; Sharif et al. 2015; Banger et al. 2018).

Unlike many developed agricultural countries, Pakistan is not up to the mark in terms of irrigation scheduling and optimization of water use efficiency, although a number of scientists are working on crop and climate simulation and water relations in Pakistan (Khaliq et al. 2012; Mubeen et al. 2012, 2013a, 2013b, 2013c; Nasim et al. 2012; Wajid et al. 2014; Amin et al. 2017, 2018a, 2018b, 2018c). This is a dire need for work to be carried out on climate change and its impacts on the water balance and in turn on crop productivity of cotton-wheat cropping systems using simulation modeling (DSSAT) in semi-arid conditions of Vehari in Southern Punjab (Pakistan) as this area is called the King of Cotton in Pakistan. Wheat is also grown as a major crop in this region.

The objectives of the experiment were to: (a) use the DSSAT model for phenology and yield prediction of different cotton and wheat cultivars under semi-arid conditions of Vehari; (b) analyze simulated phenology, yield and critical water balance parameters (using the DSSAT model) of cotton and wheat under the climatic conditions of 1 × CO2 concentration (conc.) (current), and 2 × CO2 conc. (future) in cotton-wheat cropping systems in semi-arid regions.

METHODOLOGY

Location of experimental field and soil description

The experimental field was situated at the Research Experimental Area, COMSATS University Islamabad, Vehari Campus, located around latitude 30.08 °N, longitude 72.38 °E and 136 m above sea level. It is an area having low precipitation. The long-term average annual precipitation in this region is 305 mm and the average annual temperature is 28 °C; these conditions show that this is a semi-arid area. The study area map is presented in Figure 1.

Figure 1

Study area map.

Figure 1

Study area map.

The parameters of soil which were determined during soil analysis included soil texture, bulk density (BD), and soil chemistry (Table 1). A speedy moisture meter was used to determine the soil moisture content of the soil for all the experimental plots. The soil water content was also obtained by gravimetric methods prior to planting to determine the saturation percentage and hence the field capacity of the field. These readings were helpful for providing input data for the model SBuild file (Soler et al. 2007).

Table 1

Physicochemical properties of soil in experimental area

Soil determinationUnit0–15 cm15–30 cm
Physical analysis 
 Sand 60 63 
 Silt 17 20 
 Clay 23 17 
Textural class  Sandy clay loam  
Chemical analysis 
 pH – 8.3 8.5 
 Soil EC Sm−1 1.71 1.60 
 Nitrogen 0.06 0.05 
 Phosphorus ppm 9.0 6.7 
 Potassium ppm 160 122 
Soil determinationUnit0–15 cm15–30 cm
Physical analysis 
 Sand 60 63 
 Silt 17 20 
 Clay 23 17 
Textural class  Sandy clay loam  
Chemical analysis 
 pH – 8.3 8.5 
 Soil EC Sm−1 1.71 1.60 
 Nitrogen 0.06 0.05 
 Phosphorus ppm 9.0 6.7 
 Potassium ppm 160 122 

Field capacity was measured through gravimetric methods on the base of saturation percentage. The calculation of moisture content at field capacity on weight basis (θw) from this data and then its conversion to volumetric basis (θv) using BD of the experimental soil is given in Table 2. This conversion was necessary in order to use the speedy moisture meter (the soil moisture meter measured volumetric water content of soil) which measures θ on a volumetric basis.

Table 2

Conversion of field capacity from weight (θw) to volumetric basis (θv)

Depth (cm)Field capacity on weight basis (θw)Bulk densityField capacity on volumetric basis θv = θw × B.D
0–15 28.5 1.15 32.8 
15–30 26.5 1.21 32.1 
Depth (cm)Field capacity on weight basis (θw)Bulk densityField capacity on volumetric basis θv = θw × B.D
0–15 28.5 1.15 32.8 
15–30 26.5 1.21 32.1 
Soil moisture content (on a volumetric basis) of each plot was monitored on a daily basis using a soil moisture meter. Once the moisture content became 50% less than the field capacity, irrigation water was applied to meet the growing requirement. To apply any specific depth of water (d) for a treatment, the following formula was used (Equation (1)):
formula
(1)
whereas R = water requirement on any day; FC= percent moisture content at field capacity (volumetric basis); MC = percent moisture content (volumetric basis) as determined on sampling day; D= depth of root zone (cm).
The three selected cultivars of cotton (MNH-886, Lalazar, IUB-2013) and wheat (Lassani-2008, Faisalabad-2008, AAS-2011) in the semi-arid region of Vehari were sown to obtain experimental data for model calibration (2016–17) and validation (2017–18). Simulation performance was evaluated by calculating the root mean square error (RMSE) (Wallach & Goffinet 1989) and error percentage which were calculated using Equations (2) and (3):
formula
(2)
formula
(3)
where Pi is the predicted value and Oi is the observed value for the studied variables, n is the number of observations. If these indices are found satisfactory, the DSSAT model could be used for simulating different irrigation regimes for improving water use efficiency of wheat-maize cropping system in semi-arid conditions (Vehari).

Crop management of the two crops and the model used for individual crop under DSSAT is briefly described below.

Cotton

Crop management

Cotton (Gossypium hirsutum L.) was sown in the kharif season on a laser-leveled field. The experiment layout was randomized complete block design (three replications each of MNH-886, Lalazar, and IUB-2013). A buffer plot of 1.5 m between plots of any two cultivars in a replication was kept in order to save one treatment from seepage effects of irrigation of the other treatment. The crop was sown evenly at 75 cm apart, by bed-furrow method; the seed rate was 25 kg ha−1. Thinning was done after crop emergence to have a uniform p × p distance of 30 cm. When the field capacity dropped to 50%, a given quantity of water was applied to maintain the moisture level suitable for crop growing (for the purpose of field capacity measurement, a moisture meter was used).

For applying a given quantity of water, a cut throat flume (8 inch × 3 ft) was used. The flume calculates the watercourse discharge; this discharge was used in the formula (Equation (4)) for time calculation of a specific water depth (Choudhry 2008):
formula
(4)
where t = time to irrigate (s); A = area of the plot to be irrigated (m2); d = depth of water to be applied (m); Q = discharge of the cut throat flume (m3).

The precipitation was also measured to count towards the water cycle fluctuation in the soil profile and was used as input in the model. Crop management was carried out with optimum fertilization (according to soil analysis) and plant protection (weeds as well as insect pest management).

CROPGRO-Cotton

Field data obtained from the experiments of the two growing seasons were utilized for calibration and validation of the CSM-CROPGRO-Cotton model. Soil, weather, plant related characteristics and crop management data were obtained from the experiment and used as input data in the model. A number of coefficients control the growth, phenology and seed cotton yield in the CROPGRO-Cotton model. In order to select the most suitable set of coefficients, an iterative approach was used (Wajid et al. 2014).

Wheat

Crop management

The wheat crop (Triticum aestivum L.) was sown during the winter season after the cotton crop. The experiment was laid out in a randomized complete block design (RCBD), having three replications each of the three cultivars (Lassani-2008, Faisalabad-2008, AAS-2011). A buffer plot of 1.5 m between two plots in a replication was kept in order to save one treatment from the seepage effect of irrigation of the other treatment. ‘Rouni’ (soaking) irrigation was given to the experimental field about 1 week before sowing in order to bring the soil moisture to field capacity. Deep ploughing was carried out with a chisel plough and then two cultivations and planking were completed for seedbed preparation. The sowing was carried out with a single row hand drill. The seeding density was 200 grains m−2. The crop was managed with optimum fertilization (according to soil analysis) and plant protection. Half of nitrogen and whole phosphorus and potash was applied at sowing as basal dose while the remaining nitrogen was applied at the first irrigation (Anwar et al. 2011; Mubeen et al. 2013c). When the field capacity dropped to 50%, a given quantity of water was applied to maintain the moisture level appropriate for crop growing (for the purpose of field capacity measurement, a moisture meter was used). The summation of this water applied was used to calculate water use efficiency for each cultivar of wheat. For application of known water quantity, a cut throat flume was used (as described above under section ‘Crop management’ of ‘Cotton’).

CERES-Wheat model

The dynamic crop growth model CSM-CERES-Wheat was used in the study as it can simulate daily crop growth, development and yield under conditions of varied climate and soil with diverse agronomic management practices, as discussed earlier.

Different input data are needed to prepare and run the model simulation. The minimum data set of weather (including daily minimum and maximum temperatures, solar radiation, and precipitation) was obtained from the NASA website since there is no automatic weather station in Vehari, and management data was acquired from field experiments (Amin et al. 2017). The genetic coefficients of CERES-Wheat define the definite growth and development of a given crop cultivar. These were set during calibration of the model by an optimizing procedure (Alexandrov et al. 2002).

CLIMATE CHANGE IMPACT ASSESSMENT

The DSSAT model was used to evaluate the possible effects of changing climate on yield of the two crops under agro-ecological conditions of Vehari. The seasonal driver of the model (using long-term weather data) was useful to determine the efficiency of cotton-wheat cropping systems under changing climates. The DSSAT model simulates the yield in water scarce conditions by calculating potential evaporation (Eo) which was estimated by Priestley & Taylor (1972).

RESULTS AND DISCUSSION

Model calibration

CROPGRO-Cotton model

The first year's data were used for model calibration. The genetic coefficients of the three cultivars of cotton during calibration of the model are given in Table 3.

Table 3

Genetic coefficients of cotton cultivars during model calibration

VRNAMECSDLPPSENEM-FLFL-SHFL-SDFL-SDFL-LFLFMAXSLAVRSIZLFXFRTWTPSDSFDURSDPDVPODURTHRSHSDPROSDLIP
MNH-886 22 0.01 45.4 12.5 15.4 55 74.9 1.4 166 105 1.47 0.17 33 26 2.8 94.4 0.11 0.12 
Lalazar 22 0.01 38.8 12.7 16.3 55 74.9 1.5 171 181 1.47 0.17 33 26 2.8 94.4 0.11 0.12 
IUB-2013 22 0.01 48.6 12.4 17.1 60 74.9 1.6 176 171 1.24 0.17 33 26 2.8 75.1 0.12 0.11 
VRNAMECSDLPPSENEM-FLFL-SHFL-SDFL-SDFL-LFLFMAXSLAVRSIZLFXFRTWTPSDSFDURSDPDVPODURTHRSHSDPROSDLIP
MNH-886 22 0.01 45.4 12.5 15.4 55 74.9 1.4 166 105 1.47 0.17 33 26 2.8 94.4 0.11 0.12 
Lalazar 22 0.01 38.8 12.7 16.3 55 74.9 1.5 171 181 1.47 0.17 33 26 2.8 94.4 0.11 0.12 
IUB-2013 22 0.01 48.6 12.4 17.1 60 74.9 1.6 176 171 1.24 0.17 33 26 2.8 75.1 0.12 0.11 

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 short-day plants) (1/hour); EM-FL, Time among plant appearance and flower emergence (R1) (photothermal days); FL-SH, Time among first flower and first pod (R3) (photothermal days); SD-PM, Time among first seed (R5) and physiological maturity (R7) (photothermal days); FL-SD, Time among first flower and first seed (R5) (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; SIZLF, Maximum size of full leaf (three leaflets) (cm2); XFRT, Maximum division of daily development that is partitioned to seed + shell; SLAVR, precise leaf area of cultivar under average growth situation (cm2/g); WTPSD, Maximum weight per seed (g) S; SDPDV, standard seed per pod under standard growing situation (#/pod); FDUR, Seed filling interval for pod cohort at standard growth situation (photothermal 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; PODUR, Time required for cultivar to reach final pod load under optimal conditions (photo thermal days); SDPRO, Fraction protein in seeds (g(protein)/g(seed)); SDLIP, Fraction oil in seeds (g(oil)/g(seed).

The CROPGRO-Cotton model showed good prediction in simulating crop phenology (days for anthesis and maturity) and crop yield (total dry matter, seed cotton yield, harvest index) for the experimental site by estimation of cultivar coefficients. Model simulations indicated that the crop reached the anthesis stage 67–70 days after sowing in the three cultivars and the observed days range of 64–70 days showed that the model was suitable and operated well in the environmental conditions of Vehari. Error values showed that model simulations were good under the given set of cultivar coefficients. The days to maturity observed were 155–164 days in the three cultivars; whereas the days to maturity simulated by the model for MNH-886, Lalazar and IUB-2013 were in the range of 160–167 (having an error percentage of 0.6–4.4%) (Table 4).

Table 4

Summary of observed and simulated results of cotton cultivars and their % error in model calibration

VariableUnitCultivarsObservedSimulated%ErrorRMSE
Anthesis Day MNH-886 67 68 0.7 
Lalazar 64 67 4.7 
IUB-2013 70 70 0.0 
Maturity Day MNH-886 164 165 0.6 
Lalazar 155 160 3.2 
IUB-2013 160 167 4.4 
Total dry matter kg ha−1 MNH-886 7,595 8,360 10.1 765 
Lalazar 6,760 7,399 9.5 639 
IUB-2013 7,063 7,733 9.5 670 
Seed cotton yield kg ha−1 MNH-886 1,935 2,015 4.1 80 
Lalazar 1,761 1,854 5.3 93 
IUB-2013 1,829 1,914 4.7 86 
HI MNH-886 25 24 −5.4 −1 
Lalazar 26 25 −3.8 −1 
IUB-2013 26 25 −4.4 −1 
VariableUnitCultivarsObservedSimulated%ErrorRMSE
Anthesis Day MNH-886 67 68 0.7 
Lalazar 64 67 4.7 
IUB-2013 70 70 0.0 
Maturity Day MNH-886 164 165 0.6 
Lalazar 155 160 3.2 
IUB-2013 160 167 4.4 
Total dry matter kg ha−1 MNH-886 7,595 8,360 10.1 765 
Lalazar 6,760 7,399 9.5 639 
IUB-2013 7,063 7,733 9.5 670 
Seed cotton yield kg ha−1 MNH-886 1,935 2,015 4.1 80 
Lalazar 1,761 1,854 5.3 93 
IUB-2013 1,829 1,914 4.7 86 
HI MNH-886 25 24 −5.4 −1 
Lalazar 26 25 −3.8 −1 
IUB-2013 26 25 −4.4 −1 

While discussing total dry matter during model calibration, it was found that the model showed a percentage error of 9.5–10.1% due to the observed values of 6,760–7,595 kg ha−1 and simulated values of 7,399–8,360 kg ha−1. Similarly, the model simulation error for seed cotton yield was 4.1–5.3% because the observed vs simulated values were in the range of 1,761–1,935 and 1,854–2,015 kg ha−1, respectively. However, in the case of the harvest index, some underestimation was noted during model simulation and an error percentage of −3.8 to −5.4 was obtained (Table 4).

CERES-Wheat model

The model was calibrated with the first year's data (anthesis days, total dry matter, maturity days and maturity yield). The genetic coefficients of the three cultivars of wheat during the model calibration are provided in Table 5.

Table 5

Genetic coefficients of wheat cultivars during model calibration

VRNAMEP1 VP1DP5G1G2G3PHINT
1234567
Lassani-2008 30 81 303 19 39 7.2 114 
Fasilabad-2008 30 83 305 20 39 8.2 112 
AAS-2011 29 82 304 20 38 7.2 112 
VRNAMEP1 VP1DP5G1G2G3PHINT
1234567
Lassani-2008 30 81 303 19 39 7.2 114 
Fasilabad-2008 30 83 305 20 39 8.2 112 
AAS-2011 29 82 304 20 38 7.2 112 

P1 V, Days, optimum vernalizing temperature, required for vernalization; P1D, Photoperiod response (% reduction in rate/10 h drop in pp); P5, Grain filling (excluding lag) phase duration (°C.d); G1, Kernel number per unit canopy weight at anthesis (#/g); G2, Standard kernel size under optimum conditions (mg); G3, Standard, non-stressed mature tiller wt (including grain) (g dwt).

The CERES-Wheat model presented good results in simulating crop phenology and crop yield parameters for the experimental site. Model simulations showed that the crop reached the anthesis stage 104 days after sowing in the three cultivars and the observed days were in the range of 100–103 days which showed that the model was suitable and worked well in Vehari conditions with an error of 2–5%. Satisfactory results were found in the case of days to maturity; the observed values were 138–140 days in the three cultivars; whereas days simulated by the model for Lassani-2008, Faisalabad-2008 and AAS-2011 were 144 (having an error percentage of 3–4.4%) (Table 6).

Table 6

Summary of observed and simulated results of wheat cultivars and their % error during model calibration

VariableUnitCultivarsObservedSimulated%errorRMSE
Anthesis Day Lassani-2008 103 104 1.5 
Faisalabad-2008 102 104 2.5 
AAS-2011 100 104 4.5 
Maturity Day Lassani-2008 140 144 3.0 
Faisalabad-2008 138 144 4.4 
AAS-2011 140 144 3.0 
Total dry matter kg ha−1 Lassani-2008 10,191 10,509 3.1 318 
Faisalabad-2008 9,903 10,521 6.2 618 
AAS-2011 10,365 10,513 1.4 148 
Maturity yield kg ha−1 Lassani-2008 5,118 5,289 3.3 171 
Faisalabad-2008 4,885 5,197 6.4 312 
AAS-2011 4,827 4,955 2.6 128 
HI Lassani-2008 56 56 0.0 
Faisalabad-2008 49 49 0.1 
AAS-2011 47 47 1.2 
VariableUnitCultivarsObservedSimulated%errorRMSE
Anthesis Day Lassani-2008 103 104 1.5 
Faisalabad-2008 102 104 2.5 
AAS-2011 100 104 4.5 
Maturity Day Lassani-2008 140 144 3.0 
Faisalabad-2008 138 144 4.4 
AAS-2011 140 144 3.0 
Total dry matter kg ha−1 Lassani-2008 10,191 10,509 3.1 318 
Faisalabad-2008 9,903 10,521 6.2 618 
AAS-2011 10,365 10,513 1.4 148 
Maturity yield kg ha−1 Lassani-2008 5,118 5,289 3.3 171 
Faisalabad-2008 4,885 5,197 6.4 312 
AAS-2011 4,827 4,955 2.6 128 
HI Lassani-2008 56 56 0.0 
Faisalabad-2008 49 49 0.1 
AAS-2011 47 47 1.2 

As regards total dry matter during model calibration, it was found that the model showed a percentage error of 1.4–6.2% due to the observed values of 9,903–10,365 kg ha−1 and simulated values of 10,509–10,521 kg ha−1. Similarly, the model simulation error for grain yield was 2.6–6.4% because the observed vs simulated values were in the range of 4,827–5,118 and 4,955–5,289 kg ha−1, respectively. In the case of harvest index, an error percentage of 0–1 was obtained (Table 6).

Model evaluation

CROPGRO-Cotton model

Evaluation of the model was carried out with the second year of data. During evaluation it was found that the anthesis observed days were 67–72 but the simulated values were 70–72 days showing an error percentage of 0–5.1%. After anthesis, the role of maturity days is important in cotton. We observed 160–171 days compared to the simulated values of 165–172 in the three cultivars (Table 7).

Table 7

Summary of observed and simulated results of cotton cultivars and their % error during model evaluation

VariableUnitCultivarsObservedSimulated%ErrorRMSE
Anthesis Day MNH-886 70 71 0.8 
Lalazar 67 70 5.1 
IUB-2013 72 72 0.0 
Maturity Day MNH-886 171 172 0.7 
Lalazar 160 165 3.6 
IUB-2013 163 171 5.1 
Total dry matter kg ha−1 MNH-886 7,747 8,652 11.7 905 
Lalazar 7,098 7,795 9.8 698 
IUB-2013 7,346 8,098 10.2 752 
Seed cotton yield kg ha−1 MNH-886 1,993 2,085 4.6 92 
Lalazar 1,796 1,906 6.1 110 
IUB-2013 1,920 2,014 4.9 94 
HI MNH-886 26 24 −6.3 −2 
Lalazar 25 24 −3.4 −1 
IUB-2013 26 25 −4.9 −1 
VariableUnitCultivarsObservedSimulated%ErrorRMSE
Anthesis Day MNH-886 70 71 0.8 
Lalazar 67 70 5.1 
IUB-2013 72 72 0.0 
Maturity Day MNH-886 171 172 0.7 
Lalazar 160 165 3.6 
IUB-2013 163 171 5.1 
Total dry matter kg ha−1 MNH-886 7,747 8,652 11.7 905 
Lalazar 7,098 7,795 9.8 698 
IUB-2013 7,346 8,098 10.2 752 
Seed cotton yield kg ha−1 MNH-886 1,993 2,085 4.6 92 
Lalazar 1,796 1,906 6.1 110 
IUB-2013 1,920 2,014 4.9 94 
HI MNH-886 26 24 −6.3 −2 
Lalazar 25 24 −3.4 −1 
IUB-2013 26 25 −4.9 −1 

Observed total dry matter of MNH-886, Lalazar and IUB-2013 was 7,747, 7,098 and 7,346 kg ha−1 as against the simulated values of 8,652, 7,795 and 8,098 kg ha−1, respectively, giving an error range of 9.8–11.7%. Observed values of seed cotton yield of the three cultivars were 1,993, 1,796 and 1,920 kg ha−1 compared to the simulated values of 2,085, 1,906 and 2,014 kg ha−1 respectively. The observed value of the harvest index for the three cultivars was 25–26% versus simulated values of 24–25% giving an error percentage of −3.4 to −6.3% (Table 7).

CERES-Wheat model

The CERES-Wheat model evaluation was carried out with the second year of data. During evaluation it was found that the anthesis observed days were 104–109 but the simulated values were 108–111 days showing an error percentage of 1.6–4.3%. In the case of days to maturity, we observed 144–147 days compared to the simulated values of 149–152 in the three cultivars (Table 8).

Table 8

Summary of observed and simulated results of wheat cultivars and their % error during model evaluation

VariableUnitCultivarsObservedSimulated%errorRMSE
Anthesis Day Lassani-2008 109 111 1.6 
Faisalabad-2008 107 110 2.5 
AAS-2011 104 108 4.3 
Maturity Day Lassani-2008 147 152 3.1 
Faisalabad-2008 144 151 4.8 
AAS-2011 144 149 3.3 
Total dry matter kg ha−1 Lassani-2008 10,497 10,864 3.5 367 
Faisalabad-2008 10,497 11,172 6.4 675 
AAS-2011 10,884 11,048 1.5 164 
Maturity yield kg ha−1 Lassani-2008 5,323 5,517 3.6 194 
Faisalabad-2008 5,032 5,391 7.1 359 
AAS-2011 5,117 5,256 2.7 139 
HI Lassani-2009 57 57 0.0 
Faisalabad-2009 48 48 0.7 
AAS-2012 47 48 1.2 
VariableUnitCultivarsObservedSimulated%errorRMSE
Anthesis Day Lassani-2008 109 111 1.6 
Faisalabad-2008 107 110 2.5 
AAS-2011 104 108 4.3 
Maturity Day Lassani-2008 147 152 3.1 
Faisalabad-2008 144 151 4.8 
AAS-2011 144 149 3.3 
Total dry matter kg ha−1 Lassani-2008 10,497 10,864 3.5 367 
Faisalabad-2008 10,497 11,172 6.4 675 
AAS-2011 10,884 11,048 1.5 164 
Maturity yield kg ha−1 Lassani-2008 5,323 5,517 3.6 194 
Faisalabad-2008 5,032 5,391 7.1 359 
AAS-2011 5,117 5,256 2.7 139 
HI Lassani-2009 57 57 0.0 
Faisalabad-2009 48 48 0.7 
AAS-2012 47 48 1.2 

The observed total dry matter of Lassani-2008, Faisalabad-2008 and AAS-2011 was 10,497, 10,497 and 10,884 kg ha−1 compared to the simulated values of 10,964, 11,172 and 11,048 kg ha−1, respectively, giving an error range of 1.5–6.4%. The observed values of seed cotton yield of the three cultivars were 5,323, 5,032 and 5,117 kg ha−1 compared to the simulated values of 5,517, 5,391 and 5,256 kg ha−1 respectively. The observed value of the harvest index for the three cultivars was 47–57 versus simulated values of 48–57% giving an error percentage of 0–1% (Table 8).

Relationship of simulated and observed total dry matter and maturity yield

Cotton

Simulated total dry matter production was strongly positive and linearly related with the observed total dry matter production in cotton and the regression accounted for 89.11% when the data of calibration and evaluation were pooled (Figure 2(a)). Similarly, the relationship of simulated seed cotton yield and observed seed cotton yield for the pooled data of two years was also linear and strongly positive giving an R2 value of 94.99% (Figure 2(b)). These relationships show that the CSM-CROPGRO-Cotton model worked well in the growing conditions of Vehari.

Figure 2

Relationship between simulated and observed (a) total dry matter and (b) grain yield in cotton.

Figure 2

Relationship between simulated and observed (a) total dry matter and (b) grain yield in cotton.

Wheat

In the case of wheat, simulated total dry matter production was strongly positive and linearly related with observed total dry matter production and the regression accounted for 77.56% when the data of calibration and evaluation were pooled (Figure 3(a)). Similarly, the relationship of simulated grain yield and observed grain yield was also linear and strongly positive giving an R2 value of 85.77% for the pooled data of two years (Figure 3(b)). So, like the CSM-CROPGRO-Cotton model, these relationships also show that the simulation of the CSM-CERES-Wheat model was good in the growing conditions of Vehari. So, DSSAT may be used for the simulation of yield of cotton-wheat cropping patterns in the changed climate scenarios under semi-arid environments in Southern Punjab.

Figure 3

Relationship between simulated and observed (a) total dry matter and (b) grain yield in wheat.

Figure 3

Relationship between simulated and observed (a) total dry matter and (b) grain yield in wheat.

Climate change impacts on cotton-wheat cropping pattern

The impacts of climate change on phenology, yield, crop ET and water use efficiency of cotton-wheat cropping pattern were assessed with the use of CSM-CROPGRO-Cotton and CSM-CERES-Wheat models run with weather series presenting both the present and changed climates. In order that the findings obtained by a comparison of model yields for different climates are reliable and more realistic, multi annual crop model simulations were run for each scenario. The crop simulations were run with observed pedological, physiological and cultivation data specific for each individual year and site. Observed weather series for the first year were used in present climate simulations. The weather series for simulations in the future changed climate were obtained by a direct modification in 30 years observed weather series.

Table 9 shows the model simulations which elaborate the impact of change in CO2 concentration, i.e. from 410 ppm (current CO2 conc. or 1 × CO2) to 2 × CO2 on phenology, maturity yield and total dry matter of cotton and wheat under the experimental site of Vehari. It is clear from Table 9 that the cotton cultivar IUB-2013 showed greater decrease in days to anthesis (−7.3%). On average the three cotton cultivars showed a decrease of −6.2% (i.e. from 68 days of anthesis to 64 days of anthesis). Similarly, the wheat cultivar Faisalabad-2008 showed a greater decrease in days to anthesis as compared to other cultivars. Overall, wheat cultivars took 98 days under the changed scenario of CO2. Regarding days to maturity, a similar trend was observed and an increase in CO2 concentrations reduced the duration of both the crops. In the case of cotton, IUB-2013 matured earlier than the other two cotton cultivars. The three cultivars of cotton, on average, showed a decrease of −5.7% (matured 154 days after sowing). Wheat cultivars showed an almost similar decreasing trend and an average −7.5% decrease was observed in days to maturity, i.e. in 2 × CO2 conc. the wheat crop matured in 133 days.

Table 9

Impact of CO2 concentrations on phenology and yield of cotton cultivars grown in Vehari conditions simulated through DSSAT

VariableCotton cultivars1 × CO22 × CO2Dif. w.r.t. current CO2 (%)Wheat cultivars1 × CO22 × CO2Dif. w.r.t. current CO2 (%)
Anthesis (days) MNH-886 68 64 −5.1 Anthesis (days) Lassani-2008 104 99 −5.3 
Lalazar 67 63 −6.2 Faisalabad-2008 104 98 −6.4 
IUB-2013 70 65 −7.3  AAS-2011 104 98 −6.1 
Average 68 64 −6.2  Average 104 98 −5.9 
Maturity (days) MNH-886 165 155 −5.9 Maturity (days) Lassani-2008 144 133 −7.5 
Lalazar 160 152 −5 Faisalabad-2008 144 134 −7.4 
IUB-2013 167 156 −6.3  AAS-2011 144 133 −7.7 
Average 164 154 −5.7  Average 144 133 −7.5 
Total dry matter (kg ha−1MNH-886 8,360 8,878 6.2 Total dry matter (kg ha−1Lassani-2008 10,509 11,276 7.3 
Lalazar 7,399 7,909 6.9 Faisalabad-2008 10,521 11,205 6.5 
IUB-2013 7,733 8,359 8.1  AAS-2011 10,513 11,323 7.7 
Average 7,831 8,382 7.1  Average 10,514 11,268 7.2 
Seed cotton yield (kg ha−1MNH-886 2,015 2,162 7.3 Grain yield (kg ha−1Lassani-2008 5,289 5,707 7.9 
Lalazar 1,854 1,967 6.1 Faisalabad-2008 5,197 5,550 6.8 
IUB-2013 1,914 2,079 8.6  AAS-2011 4,955 5,411 9.2 
Average 1,928 2,069 7.3  Average 5,147 5,556 8.0 
Crop ET MNH-886 526 505 −4.1 Crop ET Lassani-2008 415 391 −5.7 
Lalazar 516 503 −2.6 Faisalabad-2008 407 386 −5.1 
IUB-2013 521 503 −3.4  AAS-2011 411 389 −5.2 
Average 521 504 −3.4  Average 411 389 −5.3 
WUESY MNH-886 0.37 0.43 16.2 WUEGY Lassani-2008 1.23 1.46 18.6 
Lalazar 0.34 0.39 14.6 Faisalabad-2008 1.2 1.44 19.7 
IUB-2013 0.35 0.41 17.6  AAS-2011 1.18 1.39 18.2 
Average 0.35 0.41 16.1   1.25 1.43 14.6 
VariableCotton cultivars1 × CO22 × CO2Dif. w.r.t. current CO2 (%)Wheat cultivars1 × CO22 × CO2Dif. w.r.t. current CO2 (%)
Anthesis (days) MNH-886 68 64 −5.1 Anthesis (days) Lassani-2008 104 99 −5.3 
Lalazar 67 63 −6.2 Faisalabad-2008 104 98 −6.4 
IUB-2013 70 65 −7.3  AAS-2011 104 98 −6.1 
Average 68 64 −6.2  Average 104 98 −5.9 
Maturity (days) MNH-886 165 155 −5.9 Maturity (days) Lassani-2008 144 133 −7.5 
Lalazar 160 152 −5 Faisalabad-2008 144 134 −7.4 
IUB-2013 167 156 −6.3  AAS-2011 144 133 −7.7 
Average 164 154 −5.7  Average 144 133 −7.5 
Total dry matter (kg ha−1MNH-886 8,360 8,878 6.2 Total dry matter (kg ha−1Lassani-2008 10,509 11,276 7.3 
Lalazar 7,399 7,909 6.9 Faisalabad-2008 10,521 11,205 6.5 
IUB-2013 7,733 8,359 8.1  AAS-2011 10,513 11,323 7.7 
Average 7,831 8,382 7.1  Average 10,514 11,268 7.2 
Seed cotton yield (kg ha−1MNH-886 2,015 2,162 7.3 Grain yield (kg ha−1Lassani-2008 5,289 5,707 7.9 
Lalazar 1,854 1,967 6.1 Faisalabad-2008 5,197 5,550 6.8 
IUB-2013 1,914 2,079 8.6  AAS-2011 4,955 5,411 9.2 
Average 1,928 2,069 7.3  Average 5,147 5,556 8.0 
Crop ET MNH-886 526 505 −4.1 Crop ET Lassani-2008 415 391 −5.7 
Lalazar 516 503 −2.6 Faisalabad-2008 407 386 −5.1 
IUB-2013 521 503 −3.4  AAS-2011 411 389 −5.2 
Average 521 504 −3.4  Average 411 389 −5.3 
WUESY MNH-886 0.37 0.43 16.2 WUEGY Lassani-2008 1.23 1.46 18.6 
Lalazar 0.34 0.39 14.6 Faisalabad-2008 1.2 1.44 19.7 
IUB-2013 0.35 0.41 17.6  AAS-2011 1.18 1.39 18.2 
Average 0.35 0.41 16.1   1.25 1.43 14.6 

It is shown in Table 9 that an increase in total dry matter production was observed due to increased CO2 conc. under the cotton-wheat cropping system of Vehari. In the case of cotton, on average 7.1% increase was noted (from 7,831 to 8,382 kg ha−1) and among the three cultivars, a maximum increase was found in IUB-2013 (8.1%). In the case of wheat, the highest increase in total dry matter production was seen in AAS-2011 and the average increase in total dry matter in wheat was found to be 7.2% (from 10,514 kg ha−1 under the current CO2 conc. to 11,268 kg ha−1 under 2 × CO2 conc.). Similarly, as regards, maturity yield, we see that an overall increase of 7.3% (from 1,928 to 2,069 kg ha−1) was found in seed cotton yield and increase of 8% (from 5,147 to 5,556 kg ha−1) was observed in the grain yield of wheat under a changed CO2 scenario. The maximum increase of 8.6% was found in cotton cultivar IUB-2013, however, MNH-886 was still higher yielding as compared to other cotton cultivars with a seed cotton yield of 2,162 kg ha−1. In the case of wheat, a maximum increase of 9.2% was found in wheat cultivar AAS-2011, however, Lassani-2008 was still higher yielding as compared to other wheat cultivars with a grain yield of 5,707 kg ha−1.

It is also clear from Table 9 that increasing the CO2 concentration had some decreasing effects on seasonal crop ET, i.e. crop water requirement. The decrease (average over cultivars) was −3.4% (from 521 to 504 mm) in cotton and −5.3% (from 411 to 389 mm) in wheat. In cotton, the maximum decrease was observed in MNH-886, i.e. up to −4.1%. In the case of wheat, there was not much difference among different cultivars regarding seasonal crop ET. Regarding water use efficiency (WUE) of the two crops, it was significantly increased in both cotton (on average from 0.35 to 0.41 g m−2 mm−1) and wheat (on average from 1.25 to 1.43 g m−2 mm−1). The maximum increase in WUE was observed in cotton cultivar IUB-2013, i.e. an increase of 17.6%; however, the WUE of MNH-886 was still highest among the cultivars, i.e. 0.43 g m−2 mm−1 under changed CO2 concentration. In the case of wheat, the maximum increase in WUE was given by Faisalabad-2008; however, the maximum WUE was observed in Lassani-2008, i.e. 1.46 g m−2 mm−1. Similar results were reported by Bunce (2004) who showed that higher ambient CO2 will allow a reduction of the transpiration rate through decreased stomatal conductance, especially at higher temperature. This would lead to improved water use efficiency (WUE) and thereby to a lower probability of water stress occurrence (Kimbal 1983). Trnka et al. (2004) also reported that increased CO2 contributed to the intensified photosynthesis and improved WUE.

The concept of WUE, among other parameters, had been suggested in plant breeding to identify water use efficient genotypes under changing climate regimes, heat and water deficit stress, and interactions among them. Variation among genotypes for WUE has been found in a number of crop species, including barley (Hubick & Farquhar 1989), soybean (Hufstetler et al. 2007), upland cotton and pima cotton (G. barbadense L.) (Quisenberry & McMichael 1991; Saranga et al. 2004; Fish & Earl 2009), and wheat (Ehdaie & Waines 1993; Van Den Boogaard et al. 1997; Siahpoosh et al. 2011). In our study, it was also found that various genotypes of wheat and cotton had shown appreciable differences of productivity in the scenario of changed climate and utmost attention should be given by the breeders to develop such genotypes which are water efficient to combat high temperature and scarce water conditions.

CONCLUSIONS

Water use efficiency (WUE) is the criterion by which we can assess which cultivar of cotton and wheat in cotton-wheat cropping systems of Vehari will be better under changed CO2 scenarios. The changed CO2 scenario will be an era of changed water balance and any cultivar which produces maximum yield under this changed water balance would be definitely needed in future. Climate change impacts on cotton and wheat phenology and yields, and crop ET and WUE, were determined using the DSSAT model in semi-arid conditions. After the successful calibration and evaluation of DSSAT, when it was run with changed CO2 conc., i.e. 2 × CO2 conc., the cotton cultivar MNH-886 was found to be the better utilizer of limited water resources in semi-arid conditions of Vehari and produced a higher yield per unit of water consumed. Similarly, Lassani-2008 was the better one among all the wheat cultivars in terms of WUE. Although much research has been conducted on sowing date adjustments for mitigating the adverse effects of climate change, future studies are needed to develop such cultivars of cotton and wheat which are water efficient in order to cope with the changing hydrological balance. The farming community and decision makers would benefit from this assessment.

ACKNOWLEDGEMENTS

The authors are grateful to COMSATS University Islamabad, Pakistan for their financial support for the study work through grant No. 16-60/CRGP/CIIT/VEH/15/742.

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