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MODELING ABOVEGROUND BIOMASS ACCUMULATION OF COTTON.

Byline: B. Jia H. B. He F. Y. Ma M. Diao G. Y. Jiang Z. Zheng J. Cui and H. Fan

ABSTRACT

The objective of this study was to develop an improved model for describing the accumulation of aboveground cotton biomass. The model input was RTEP which was the normalized product of thermal effectiveness and photosynthetically active radiation. The model was calibrated using data from field plots with five N rates and two cotton cultivars. Model validation was conducted using data from three independent cotton fields. Eight nonlinear functions described cotton growth well (Rgreater than 0.0.894 SDless than 0.05). The parameters of the functions were then compared and the results indicated that the Richards function best fit the nonlinear relationships in a biologically meaningful way. The equation was as follows: relative aboveground biomass accumulation (RAGBA) = 1.024/(1+e6.646-10.115RTEP)1/1.417 (r = 0.981 s = 0.043). Validation results indicated that the root mean square error was 0.659 t hm-2 the relative error was 5.337% the coefficient of concordance was 0.988 and the coefficient of determination was 0.961. The second derivative of the optimized model showed that in cotton the process of aboveground biomass accumulation could be divided into three phrases using two inflection points. When the accumulation rate of the aboveground biomass of cotton was at its maximum the relative product of thermal effectiveness and PAR was 0.622 the maximum rate of the aboveground biomass accumulation was2.299 and the aboveground biomass accumulation was 0.549. In conclusion our study indicates that the product of thermal effectiveness and PAR is a valuable parameter for estimating aboveground biomass accumulation in cotton.

Key words: Cotton Aboveground biomass accumulation Product of thermal effectiveness and PAR (TEP) Normalization Richards Model

INTRODUCTION

Plant development and biomass accumulation are complex and multifaceted processes (Arshadullah et al. 2009; Meade et al. 2013). An accurate model of biomass accumulation in plants could provide a much needed description of important aspects of plant development (Meade et al. 2013; Torrez et al. 2013). Such a model could describe and predict that which would be difficult to obtain experimentally. In addition dynamic simulation models of plant growth would be an invaluable aid in the decision-making process of farmers.A good model is flexible enough for use under a variety of environmental conditions and simple enough that it can be included in more complex models of whole plant growth (Meade et al. 2013). Many investigators have examined the link between climate and plant biomass accumulation with the observed climate actingas a boundary forcing for the plant model (Osborne et al.2007).Several methods have been developed to estimate biomass accumulation of crops. Most of these use the segmented model approach which combines multiple linear models to describe biomass accumulation (Meade et al. 2013). The use of segmented models which assumes that each stage of growth is distinct from the next is perhaps the simplest method for analyzing crop growth. However segmented models cannot describe continuous biomass accumulation. Nonlinear functions are needed to model the continuous growth of crops.Nonlinear functions that model crop biomass accumulation generally have a sigmoid shape (i.e. logistic Weibull or Gompertz). Nonlinear function models of plant growth have been developed for many crops (Thornley and France 2007) including maize (Gambin et al. 2008; Meade et al. 2013) sorghum(Gambin et al. 2007) soybean (Board et al. 2005) rice(Qiao et al. 2013) wheat (Pepler et al. 2006) and safflower (Dordas et al. 2009). Recent studies have either used days after emergence (DAE) or growing degree days (GDD) as independent variables in models of biomass accumulation (Meade et al. 2013; Xue et al.2008; Yang et al. 2012). Although both inputs have been shown to be accurate predictors of biomass accumulation models using GDD are generally considered to be more accurate than models using DAE for describing the entire growth process of crops (Meade et al. 2013). The GDDmethod which utilizes thermal units was developed toimprove accuracy in estimating the physiological maturity of crops. However the GDD method does not take into consideration the effect of photosynthetic active radiation (PAR) or global radiation (Q) on crop growth. Cotton yield is generally determined by two factors: biomass accumulation and the proportion of biomass partitioned to the reproductive organs (Bange and Milroy 2004; Saleem et al. 2010; Yang et al. 2011; Yang et al. 2012). The accumulation of aboveground biomass at different growth stages directly influences vegetative and reproductive growth. The accumulation rate varies among growth stages. For example Yang et al. (2012) observed that nearly 65% of total cotton biomass accumulated between first bloom and peak bloom regardless of N application rate. The authors referred to this time as the period of fast biomass accumulation (Yang et al. 2012).In this paper we used the abbreviation TEP to represent the product of thermal effectiveness and PAR The TEP value was calculated for each day during the cotton growing season and then normalized to obtain relative TEP (RTEP). The RTEP values were then used as independent variables in non-linear models describing aboveground biomass accumulation in cotton. Nitrogen uptake is a critical factor limiting the accumulation of aboveground cotton biomass. Cotton biomass generally increases as N fertilizer application increases. Therefore the models were calibrated using data from cotton grown with five different N application rates. The specific objectives of this study were (i) to assess the relationship between aboveground cotton biomass and TEP (ii) to select the best nonlinear function for modeling the accumulation of aboveground biomass in cotton and (iii) to verify the models using data collected from three fields of high-yield cotton.

MATERIALS AND METHODS

Experiment Design: Experiment 1 consisted of plot experiments at two locations near Shihezi Xinjiang Uyghur Autonomous Region China. The plots were at the National Agricultural Hi-tech Demonstration Park in2010 and at the Shihezi University Agricultural Experiment Station in 2011. The soil at both sites is heavy loam. Selected soil properties at the two sites are shown in Table 1.Cotton was sown by hand on April 20 2010 and April 16 2011. The cotton cultivars were Xinluzao 43' (XLZ 43) and Xinluzao 48' (XLZ 48). The plant density was 2.63 A- 105 plant hm-2. The study included five N fertilizer application rates: 0 kg hm-2 (N0) 120 kg hm-2 (N1) 240 kg hm-2 (N2) 360 kg hm-2 (N3) and 480 kg hm-2 (N4). The plots were arranged in a randomized complete block design with three replicates. The area of each plot was 46 m2 (4.6 m A- 10 m).Each plot was completely covered by laying three sheets of plastic film (1.4 m wide A- 10 m long) edge to edge. Each sheet of plastic film had four rows of cotton with row spacings of 10 cm - 66 cm - 10 cm - 66 cm (Fig.1a). There were 10 cm between each hill within a row. Two drip tapes were laid under each sheet of plastic film. The emitter discharge rate was 3.2 l h-1 and the emitter spacing was 0.30 m. After sowing 600 m3 ha-1 of water was immediately applied to each plot via drip irrigation to promote germination. A total of 5400 m3 hm-2 of water was applied during the growing season. The water was divided evenly among 11 irrigation events. Waterproof membrane was buried to a depth of 60 cm around each plot to prevent water movement between plots.The plots were fertilized at planting with 75 kg K2O hm-2 as potassium chloride and 150 kg P2O5 hm-2 as calcium superphosphate. Nitrogen fertilizer (urea) was applied via drip irrigation with 10% of the N applied at planting 25% applied at budding 45% applied at blooming and 20% applied at full boll (Table 2). The remaining management practices were the same as those used by local farmers.Experiment 2 was conducted in 2012 at fields belonging to the Agricultural Modernization of Xinjiang Production and Construction Corps (4306'-4520' N). Cotton cultivar Xinluzao 45' (XLZ 45) was grown on Field 9 Company 2 105th Corp of the 6th Division. Cultivar Biaoza A1' (BZ A1) was grown on Field 3Company 5 149th Corp of the 8th Division. CultivarShiza 2' (SZ 2) was grown on Field 6 Company 19150th Corp of the 8th Division. The total area of each irrigated field was approximately 450 hm2. The cotton inall three fields was sown on April 23 2012. The plant density was 2.5 A- 105 plants hm-2. Two drip tapes were placed under the plastic film with an emitter discharge rate of 3.2 l h-1 and emitter spacings of 0.30 m. The total amount of irrigation water applied during the growing season was 5400 m3 hm-2. Fertilizer was applied at planting at the following rates: 375 kg N hm-2 as urea150 kg P2O5 hm-2 as calcium superphosphate and 75 kg K2O hm-2 as potassium chloride. All other aspects of crop management were the same as in experiment 1.

Measurement of Aboveground Biomass: In Experiment1 nine similar plants were removed from each plot every14 days from emergence to harvest during the 2010 and2011 growing seasons. In Experiment 2 30 similar plants were removed from each field every 14 days from emergence to harvest in 2012. The plants were separated into stems leaves buds blooms and bolls and open-bolls. Plant samples were dried for 30 min at105 C and then at 80 C until reaching constant weight.

Climatological Information: Climatological data in Experiment 1 was obtained from an observation station belonging to the Shihezi Weather Bureau. Photosynthetically active radiation (PAR) global radiation (Q) and temperature (T) were automatically measured every hour during the growing season. In experiment 2 climatological data was obtained by weather stations belonging to each corp.Calculation of Thermal Effectiveness: Thermal effectiveness (TE) is the ratio of crop growth for 1 dunder real temperature conditions to crop growth for 1 where To is the optimum growth temperature; Tb is thebase temperature of growth; Tm is the maximumtemperature of growth; and T is the mean temperature each hour. The values of Tb To and Tm of cotton were set as follows: from sowing to emergence Tb = 12 C To = where RAGBAi is the relative aboveground biomassaccumulation within growth stage i; AGBAi is themeasured accumulation of aboveground biomass-2 30 C and Tm = 45 C; from emergence to boll openingTb = 12 C To = 30 C and Tm = 35 C (Guan et al.2013; Hawkins et al. 2012; Suleiman et al. 2013).where Q is the photosynthesis coefficient i.e. theproportion of PAR in total solar radiation Q. The normative value is 0.5 (Wang et al. 2013).

Calculating the Product of TE and PAR: Ni et al (2009) developed a dynamic model describing the total accumulation of aboveground crop biomass. The model first multiplied hourly values of TE and PAR to obtain a value called HTEP (mol m-2 h-1) for each hour of every day during the growing season. The formula for calculating HTEP is as follows:Equation

where TE is a unitless estimate of thermal effectiveness ranging between 0 and 1 PAR is the average instantaneous photosynthesis active radiation (mol m-2 s-1) and 3600 is a constant for converting averageinstantaneous PAR into total PAR for one hour.Statistical Analysis and Model Verification: Correlation analysis between accumulated RTEP and RAGBA were performed using SPSS 17.0 software. Origin Pro 8.5 was used to prepare the figures. Curve Expert 1.4 was usedfor fitting the data. Sigma Plot 10.0 was used to draw 1:1straight line graphics. Data collected from Experiment 1 was mainly used to calibrate the regression models. Data collected from three independent fields (Experiment 2) was subsequently used to validate the regression models under different management practices. The performance of the models was estimated by comparing the differences in coefficient of determination (R2) root mean square error (RMSE) relative error (RE %) and coefficient of concordance (CC). The precision and accuracy of the models increased as R2 and CC increased and as RMSE and RE decreased. The values of RMSERE and CC were calculated using Eq.(8) Eq.(9) andEq.(10) respectively:

Table-1: Selected soil properties at the experimental fields

###Organic matter###Alkaline-N###Olsen-P###Available K###pH

Year###Location

###(mg kg1)###(mg kg1)###(mg kg1)###(mg kg1)###Value

2010###Demonstration zone###29.631.02###69.722.32###19.833.24###518.2550.31###7.80.27

2011###Test station###16.290.97###52.351.49###35.801.26###219.0234.25###7.10.21

Table-2: Fertilizer application rates in the plot experiments

###Total fertilizer###Amount applied at

###Amount of N fertilizer applied as topdressing (kg ha-2)

Treat-###(kg ha-2)###planting (kg ha-2)

ment###N###P 2O 5 K 2O###N###P 2O 5###K 2O###Bud stage###Bloom stage###Full boll stage

###Distribution ratio###10%###100%###100%###25%###45%###20%

N0###0###150###75###0###150###75###0###0###0

N1###120###150###75###12###150###75###30###54###24

N2###240###150###75###24###150###75###60###108###48

N3###360###150###75###36###150###75###90###162###72

N4###480###150###75###48###150###75###120###216###96

Table-3: Dynamic models of relative aboveground biomass accumulation in cotton

###Parameter###y value

Model###Fitted equation###R###SD

###a###b###c###d###X###x=0###x=1

Richards###Y=a/(1+eb-cx)1/d###1.024###6.645###10.115###1.417###0.983###0.042###a###0.009###1.002

Logistic###Y=a/(1+be-cx)###1.043###182.489 8.575###0.981###0.046###a###0.005###1.008

Gompterz###Y=a(e-e)b-cx###1.195###3.309###5.093###0.974###0.049###a###0###1.049

Weibull###Y=a-b(e-cx)d###0.974###0.882###8.210###5.837###0.953###0.047###a-b###0.009###0.973

MMF###Y=(ab+cxd)/( b+cxd)###0.112###0.033###1.026###8.258###0.941###0.042###c###0.011###0.994

Growth###Y=a(b-e-cx)###7.149###0.915###0.270###0.917###0.046###ab###0.054###1.079

Polynomial###Y=a+bx+cx2+dx3###1.725###-10.149 19.096###-9.713###0.901###0.043###1.382###0.958

Rational###Y=(a+bx)/(1+cx+dx2)###-0.041###0.218###-2.036###1.223###0.894###0.048###0###0.002###0.948

Table-4: Parameters of the optimal model for relative aboveground biomass accumulation of cotton grown with

###different N rates

###Parameter

Year###Cultivar###N rates###R###SD

###a###b###c###d

###N0###1.010###11.983###14.468###3.764###0.907###0.009

###N1###1.000###6.695###9.750###1.790###0.913###0.026

###XLZ 48###N2###0.999###7.941###12.501###1.934###0.942###0.019

###N3###0.999###3.677###7.208###0.774###0.967###0.022

###N4###1.000###3.735###7.607###0.722###0.935###0.021

2010

###N0###1.000###13.394###16.048###3.094###0.902###0.014

###N1###0.993###2.2051###5.589###0.392###0.924###0.017

###XLZ 43###N2###0.995###1.916###5.669###0.343###0.955###0.023

###N3###1.000###4.108###7.855###0.840###0.979###0.020

###N4###0.999###4.038###8.076###0.768###0.964###0.026

###N0###0.999###8.934###11.510###3.740###0.904###0.011

###N1###1.000###19.397###26.251###1.185###0.943###0.030

###XLZ 48###N2###1.000###11.326###16.506###1.508###0.952###0.020

###N3###1.000###11.755###17.074###1.572###0.985###0.027

###N4###0.999###10.529###15.231###1.539###0.953###0.042

2011

###N0###1.000###11.710###14.719###3.259###0.965###0.037

###N1###1.000###21.980###29.975###1.975###0.960###0.041

###XLZ 43###N2###1.000###12.844###18.353###1.799###0.984###0.024

###N3###1.000###13.804###19.845###1.838###0.978###0.034

###N4###0.999###12.385###17.723###1.949###0.961###0.041

DISCUSSION

The results showed that cotton has a sigmoid growth pattern that can be best explained using nonlinear growth models with RTEP as the independent variable in the model (Figure-3). We compared the ability of eight nonlinear functions to describe cotton growth in a biologically meaningful way. All eight functions met the three basic requirements for interpreting the accumulation of aboveground cotton biomass in a biologically meaningful way. However when RTEP was positive infinity parameter a in the Weibull MMF Growth Polynomial and Rational functions did not approach 1" (Table-3). This means that the RAGBA value predicted by the functions did not equal the total aboveground cotton biomass. Therefore these five functions were excluded from further consideration. The remaining three models were tested using RTEP values of 0" (minimum) and 1" (maximum). When RTEP = 0 the RAGBA value of Gompterz was 0 (Table-3). This had no biological meaning; therefore this model was also excluded from further consideration.The Richards model and the Logistic model both did a good job of describing cotton growth. The Logistic model has been commonly used to describe the process of biomass accumulation in cotton (Yang et al. 2011 Yang et al. 2012) and in other food crops (Melchiori and Caviglia 2008; Overman and Scholtz 2011; Pepler et al.2006). The Logistic model easily converges when placedwithin a statistical model as a regression covariate. However Logistic functions have limitations. For example they force a symmetric curve which may not be biologically relevant (Overman and Scholtz 2013). In addition symmetry assumes that the acceleration of growth after the lag phase occurs at the same rate as the slowing of growth as maturity approaches (Overman and Scholtz 2013). Overall the Logistic function does not perform as well as the Richards model.Our results clearly indicate that a Richards function can accurately estimate the accumulation of aboveground cotton biomass. Furthermore RTEP which reflects both the thermal and light conditions that cotton is growing under is valuable as an input into the Richard's model. The Richards function has been widely used to describe the growth of many crops (Meade et al.2013) thus demonstrating its flexibility (Overman and Scholtz 2013). In our study the parameters of the Richards model were related to cotton growth in both2010 and 2011 in a biologically meaningful way (Table-4). The results need to be validated under different growing conditions and at different sites. The closerelationships between the observed and simulated valuesillustrates that the Richards model showed good goodness of fit for experiment 2. The highest R2 values were obtained from the verification model (Figure-4). We concluded that the Richards function can accurately model cotton growth.For convenience we chose to normalize the observed values of AGBA and TEP at each cotton growth stage. The resulting values RAGBA and RTEP were used in the calibration of the nonlinear models. The normalized method enables researchers to obtain beneficial information. Normalization can not only overcome changes in model parameters caused by different cultivation techniques and cultivars but it can also improve the versatility of the model. This Richards model had relatively small parameters andstraightforward biological interpretation.The credibilityand universality of the model was enhanced. Future work is needed to improve the analytical method.Analytical solutions can be found from nonlinear differential equations that are based on key fundamental processes (Overman and Scholtz 2011). The first derivative (dy/dx) can be used to assess the relative characteristic values in the Richards model (i.e. ARmax ARTEP ARAGBA and RGave). Using the second derivative of the Richards model the process of aboveground biomass accumulation in cotton was split into three stages using two inflection points. During the initial (lag) stage there is little biomass accumulation (Meade et al. 2013; Sala et al. 2007a). In contrast there is rapid nearly linear accumulation in aboveground biomass during the next stage which is between RTEP1 and RTEP2 (Figure-3). During this period biomass accumulation reached approximately 60% of the total aboveground biomass accumulation during the growing season. Cotton biomass accumulated most rapidly between first bloom and peak bloom in each N treatment. This agreed with a previous report by Yang et al (2012).This period of fast growth was considered to be the key period (or sensitive period) for aboveground biomass accumulation of cotton. Soil fertility must be carefully managed during this period to ensure maximum yield (Boquet 2005; Clawson et al. 2008; Kumbhar et al. 2008; Yang et al. 2012). During the final stage cotton growth slows until a maximum weight is reached. At some point in the final stage the cotton reaches physiological maturity and ceases to accumulate aboveground biomass (Meade et al. 2013; Sala et al.2007a). Overall the second stage in the process of aboveground biomass accumulation process was most important for cotton growth and yield formation. It is especially important to provide for the N needs of the cotton crop during this time (McConnell and Mozaffari2005; Yang et al. 2012).

Conclusions: This paper describes the selection of an optimized mathematical model for describing the accumulation of aboveground cotton biomass. Eight nonlinear functions were evaluated with TEP (i.e. the product of thermal effectiveness and photosynthetically active radiation) as the input. Statistical procedures for

the comparison of nonlinear regression models were described as well as methods for incorporating the function into a crop growth model. The new model which uses Richards equation is significantly better than previous models in describing the accumulation of aboveground cotton biomass. The new model is a useful tool for monitoring the growth and N needs of cotton. Such information is important for maximizing cotton yield and quality.

Acknowledgements: The authors gratefully acknowledge the National Key Technology Support Program Project (2012BAD41B02) the Transformation of Scientific and Technological Achievements (2011GB2G410008) and the Graduate Science and Technology Innovation Projects of the Xinjiang Uyghur Autonomous Region (XJGRI2013064) in China. We are grateful for the support and assistance of Professor Fuyu Ma and Dr Haibin He.

REFERENCES

Ahmed A. U. H. R. Ali S. I. Zamir and N. Mehmood (2009). Growth yield and quality performance of cotton cultivar BH-160 (Gossypium hirsutum L.) The J. Anim. Plant Sci. 19(4): 189-192.Arshadullah M. M. Anwar and A. Azim (2009).Evaluation of various exotic grasses in semi-arid conditions of Pabbi Hills Kharian Range The J. Anim. and Plant Sci 19: 85-89.Bange M. P. and S. P. Milroy (2004). Growth and dry matter partitioning of diverse cotton genotypes Field Crop. Res. 87: 73-87.Board J. E. and H. Modali (2005). Dry matter accumulation predictors for optimal yield in soybean Crop Sci. 45(5): 1790-1799.Boquet D. J. (2005). Cotton in ultra-narrow rows pacing:plant density and nitrogen fertilizer rates Agron. J. 97: 279-287.Clawson E. L. J. T. Cothren D. C. Blouin and J. L.Satterwhite (2008). Timing of maturity in ultra-narrow and conventional row cotton as affected by nitrogen fertilizer rate Agron. J. 100:421-431.Dordas C. A. and C. Sioulas (2009). Dry matter and nitrogen accumulation partitioning and retranslocation in safflower (Carthamus tinctorius L.) as affected by nitrogen fertilizationField Crop Res 110: 35-43.Gambin B. L. and L. Borras (2007). Plasticity of sorghum kernel weight to increased assimilate availability Field Crops Res. 100: 272-284.Gambin B. L. L. Borras and M. E. Otegui (2008).Kernel weight dependence upon plant growth at different grain-lling stages in maize and sorghum Aust. J. Agric. Res. 59: 280-290. Guan H. Li J. and Li Y. (2013). Effects of drip system uniformity and irrigation amount on cotton yield and quality under arid conditions Agr. Water Manage. 124: 37-51.Hawkins E. T. M. Osborneb C. K. Hoa and J. C.Andrew (2013). Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe Agr. Forest Meteor. 170: 19-31Kumbhar A. M. U. A. Buriro S. Junejo F. C. Oad G. H.Jamro B. A. Kumbhar and S. A. Kumbhar (2008). Impact of different nitrogen levels on cotton growth yield and N-uptake planted in legume rotation Pak. J. Bot 40: 767-778.McConnell J. S. and M. Mozaffari (2005). Yield petiole nitrate and node development responses of cotton to early season nitrogen fertilization J. Plant Nutr 27 (7): 1183-1197.Meade K. A. Cooper M. and W. D. Beavis (2013).Modeling biomass accumulation in maize kernels. Field Crop Res. 151: 92-100.Melchiori R. and O. Caviglia 2008. Maize kernel growth and kernel water relations as affected by nitrogen supply Field Crops Res. 108(3):198-205.Ni J. H. X. H. Chen C. H. Chen Q. Xu and D. Q. Zhao (2009). Simulation of cucumber fruit growth in greenhouse based on production of thermal effectiveness and photosynthesis active radiation Chin. Soc. Trans. Agric. Eng. 25(5): 192-196. (In Chin.)Osborne T. M. D. M. Lawrence A. J. Challinor J. M.Slingo and T. R. Wheeler (2007). Development and assessment of a coupled cropclimate model Global Change Biol. 13(1): 169-183.Overman A. R. and R. V. III. Scholtz (2011).Accumulation of Biomass and Mineral Elements with Calendar Time by Corn: Application of the Expanded Growth Model PLoS One 6(12): e28515 doi:10.1371/journal.pone.0028515.Overman A. R. and R. V. III. Scholtz (2013).Accumulation of Biomass and Mineral Elements with Calendar Time by Cotton: Application of the Expanded Growth Model PLoS One 8(9): e72810 doi:10.1371/journal.pone. 0072810.Pepler S. M. Gooding and R. Ellis (2006). Modelling simultaneously water content and dry matter dynamics of wheat grains Field Crops Res. 95:49-63.Qiao J. L. Yang T. Yan F. Xue and D. Zhao (2013).Rice dry matter and nitrogen accumulation soil mineral N around root and N leaching with increasing application rates of fertilizer Eur. J. Agro. 49: 93-103.Sala R. G. M. E. Westgate and F. H. Andrade (2007).Source/sink ratio and the relationship between

maximum water content maximum volume and final dry weight of maize kernels Field Crops Res. 101: 19-25.Saleem M. F. M. F. Bilal M. Awais M. Q. Shahid and S.A. Anjum (2010). Effect of nitrogen on seed cotton yield and fiber qualities of cotton (Gossypium hirsutum L.) cultivars The J. Anim. Plant Sci. 20(1): 23-27.Suleiman A. A. C. M. T. Soler and G. Hoogenboom (2013). Determining FAO-56 crop coefficients for peanut under different water stress levels Irrigation Sci. 31(2): 169-178.Torrez V. P. M. JArgensen and A. E. Zanne (2013).Specific leaf area: a predictive model using dried samples Aust. J. Bot 61: 350-357Wang L. C. W. Gong Y. Y. Ma B. Hu and M. Zhang(2013). Photosynthetically active radiation and its relationshipwith global solar radiation inCentral China. Int. J. Biometeorol. 1-13.Xue X. Y. Sha W. Guo and Z. Zhou (2008).Accumulation characteristics of biomass and nitrogen and critical nitrogen concentration dilution model of cotton reproductive organ Acta Ecol. Sin 28: 6204-6211.Yang G. Z. H. Y. Tang J. Tong Y. C. Nie and X. L.Zhang (2011). Responses of cotton growth yield and biomass to nitrogen split application ratio. Eur. J. Agron. 35: 164-170.Yang G. Z. H. Y. Tang J. Tong Y. C. Nie and X. L.Zhang (2012). Effect of fertilization frequency on cotton yield and biomass accumulation Field Crops Res. 125: 161-166.
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