Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut in a subtropical region of eastern India

CJ-00222; No of Pages 9 TH E C ROP J O U R NA L XX ( 2 0 17 ) XXX–X XX

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ScienceDirect

Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut in a subtropical region of eastern India Debjani Halder a,⁎, Rabindra Kumar Pandab , Rajiv Kumar Srivastavac , Shyamal Kheroar d a

Department of Agriculture, Government of West Bengal, Mathabhanga, Cooch Behar 736 146, West Bengal, India School of Infrastructure, Indian Institute of Technology, Bhubaneswar 751 013, Odisha, India c Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721 302, West Bengal, India d Department of Agronomy, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar 736 165, West Bengal, India b

AR TIC LE I N FO

ABS TR ACT

Article history:

Projected changes in weather parameters, mainly temperature and rainfall, have already

Received 28 August 2016

started to show their effect on agricultural production. To cope with the changing scenarios,

Received in revised form 4 February

adoption of appropriate management strategies is of paramount importance. A study was

2017

undertaken to evaluate the most appropriate combination of sowing date and phosphorus

Accepted 9 February 2017

fertilization level for peanut crops grown in sandy loam soil in a subhumid region of eastern

Available online xxxx

India. Field experiments were conducted during the summer seasons of 2012 and 2013 on peanut crops at the farm of the Indian Institute of Technology, Kharagpur. The DSSAT v4.5

Keywords:

CROPGRO-Peanut model was used to predict the phenology, growth, and yield of peanut

Sowing date

crop under combinations of four sowing dates and four phosphorus fertilization levels. The

Phosphorus fertilizer

model was calibrated with a 2012 dataset of growth, phenology, and yield parameters for

CROPGRO-Peanut model

estimating the genetic coefficients of cultivar TMV-2 and was validated with a 2013 dataset

Sensitivity analysis

of the same parameters. Simulations of pod yield and other yield parameters using the

RMSE

calibrated model were found to be quite accurate. The model was able to reasonably simulate pod yield and final biomass with low normalized root mean square error (RMSEn), low absolute root mean square error (RMSEa) and high coefficient of determination (R2 > 0.7) over a wide range of sowing dates and different phosphorus fertilization levels sensitivity analysis indicated that sowing from the second week of January to the end of February with 30–50 kg P2O5 ha−1 would give the highest pod yield. © 2017 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction Eastern India, which is one of the major contributors to Indian agricultural food production, is facing various challenges owing

to weather variation in recent years. Erratic rainfall distribution, particularly during the monsoon season, is causing drought or flooding in alternate years and reduction in yield of major food crops including rainfed rice (Oryza sativa L.), and other field crops.

⁎ Corresponding author. E-mail address: [email protected] (D. Halder). Peer review under responsibility of Crop Science Society of China and Institute of Crop Science, CAAS.

http://dx.doi.org/10.1016/j.cj.2017.02.005 2214-5141 © 2017 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article as: D. Halder, et al., Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut..., The Crop Journal (2017), http://dx.doi.org/10.1016/j.cj.2017.02.005

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Accordingly, contingent crop planning using short-duration and high-value crops should be performed to protect farmers from heavy crop yield and monetary losses. Growing peanut (Arachis hypogaea L.), an important oilseed as well as a high-value cash crop, can be considered as an alternative adaptation strategy for poor farmers who depend solely on agriculture. In India, about 75% of the peanut growing area lies in a low- to moderate-rainfall zone with a short growing period (90–120 days). The crop is grown mainly in the rainy (Kharif) season (June to September), accounting for about 80% of the total peanut production. But owing to variation in monsoon rains, peanut production fluctuates in major growing areas. In the southern and eastern part of the country, peanut is cultivated during the post-monsoon and pre-monsoon season. However, if irrigation facilities are available, peanut can be planted from January to May as a summer crop. According to Bandyopadhyay et al. [25] peanut cultivation under irrigated conditions during the summer season (March– June) may increase the productivity of the crop by two to three times relative to the monsoon crop. For this reason, studies have been conducted to determine appropriate sowing dates for peanut to obtain potential yield in most peanut-growing countries worldwide [21,23,29]. Significant differences in shelling percent among sowing dates were observed, in the ranges of 66%–80% in the Kharif and 66%–73% in the Rabi season. Hundred-pod weight ranged from 74 to 107 g in the Kharif and 65 to 110 g in the Rabi [31]. On the basis of experiments conducted during all seasons, Reddy [22] reported that Rabi peanut gave an additional yield of 156% in 1989 and 134% in 1990 over the summer-season crop. In both years the harvest index was considerably higher (39.9% and 48.0%) in Rabi than in Kharif (32.7% and 37.3%) and summer (24.5% and 18.9%). Sharma and Yaday [1] stated that phosphorus fertilizer plays a beneficial role in legume growth and development. It promotes extensive root development, which improves the supply of other essential nutrients and water to the growing parts of the plants, resulting in increased photosynthetic area and accumulation of more dry matter [14,26,32,34]. In addition, the productivity of peanut is lowered in the highly acidic soil in the tropical region of the country [20,30]. The lower concentration of phosphorus fertilizer as well as the presence of toxic elements such as aluminum and lower concentrations of calcium, potassium, and magnesium reduce peanut yield [3,28]. To assess the scope for increasing peanut production in India, is prerequisite to know the yield potential of and to identify factors limiting the yield of peanut. Such knowledge can be obtained by conducting field trials over several years to evaluate crop management practices in different environments. An alternative approach is to use validated crop growth models and historical climatic data to evaluate various crop management strategies for locations or regions on a long-term basis. To meet these objectives, peanut crop models have been developed in the U.S. [10,11,15,17,18] and in India [24] to quantify growth responses to various management practices. The Cropping System Model (CSM)-CROPGRO-Peanut is a process-oriented model that is part of the Decision Support System for Agrotechnology Transfer (DSSAT) [6,12,16]. The model has been evaluated extensively for investigating multiple environmental conditions and evaluating crop yield, cultivars, cropping practices and genetic coefficient [18]. Accordingly, the validated model can be used to predict growth and yield responses to sowing dates, nutrients, row

spacing, and irrigation. The objective of the present study was to evaluate the performance of the model for peanut crops grown in subhumid and subtropical regions of eastern India and to identify appropriate combinations of sowing dates and phosphorus fertilization levels by sensitivity analysis.

2. Materials and methods 2.1. Field experiment Crop experiments were conducted in summer peanut during 2012 and 2013 at the experimental farm of the Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, India (22°19′N latitude and 87°19′E latitude; 48 m above mean sea level). The growth and yield data collected from the experiments were used to calibrate and validate the DSSAT v4.5 CROPGRO-Peanut model. In the field experiments, 16 treatment combinations, including four planting dates (January 14 and 29, February 14 and 28) and four phosphorus fertilization levels (0, 40, 60, and 80 kg P2O5 ha−1) respectively, were used and replicated three times, with plot sizes of 20 m2. The whole experiment was designed following the split-plot technique (main factor: date of sowing; subfactor: fertilizer dose). Peanut seeds of cultivar TMV-2 (Spanish, bunch-type cultivar) were treated with culture (Rhizobium japonicum) at 25 kg ha− 1 and sown at a depth of 5 cm with 30 cm row-to-row and 20 cm plant-to-plant spacing. The soil of the experimental site was red lateritic and sandy loam. The soil contained 14%–28% clay, 19%–26% silt, and 52%–59% sand with bulk density 1.56–1.62 g cm−3. The soil had pH 5.5–5.9 and 0.15%–0.32% organic carbon as well as 0.04% total nitrogen, 0.04% phosphorus and 0.42% potassium [27]. During both years, all the plots were irrigated with measured amounts of water at regular intervals of five days to 45 cm of root zone depth to maintain field capacity and ensure no water stress during the cropping season. Leaf area (cm2) plant−1 was measured with a leaf area meter (AC Mas Technocracy Pvt. Ltd., Sr. no. 1303–1161) 25 days after emergence. Daily temperature (maximum and minimum), rainfall, solar radiation for the cropping period were recorded with an automatic weather station installed in the experimental field and used as input data for the simulation model. The weather conditions at Kharagpur, expressed as monthly average maximum and minimum temperature (°C) and total rainfall (mm) during the crop growing season (January to May 2012 and 2013), are presented in Fig. 1. The maximum and minimum temperature varied in the ranges 24.5–40.0 °C and 14.4–24.7 °C, respectively, during the 2012 crop season, with total rainfall of 167 mm. During the 2013 cropping season, a total of 104 mm rainfall was received, with maximum and minimum temperatures in the ranges 24.2–33.4 °C and 12.8–28.7 °C, respectively.

2.2. Model description 2.2.1. CROPGRO-Peanut model The DSSAT CROPGRO-Peanut model v4.5 [6,12] was used to study the effect of sowing date and phosphorus fertilization level on yield and yield parameters of peanut and to identify a

Please cite this article as: D. Halder, et al., Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut..., The Crop Journal (2017), http://dx.doi.org/10.1016/j.cj.2017.02.005

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45.0

60.0

40.0 50.0

Rainfall (mm) 2012

30.0

40.0

25.0 30.0

20.0

Rainfall (mm) 2013

Rainfall (mm)

Temperature (°C)

35.0

Maximum Temp (°C) 2012 Minimum Temp (°C) 2012

20.0

15.0

Maximum Temp (°C) 2013

10.0

Minimum Temp (°C) 2013

10.0 5.0

0.0

0.0

January

February

March

April

May

Fig. 1 – Weather data (monthly average maximum and minimum temperature and monthly total rainfall).

suitable management strategy to cope with possible climate changes in eastern India. The four sowing dates and four phosphorus fertilization treatments were employed to fit the model to changing scenarios. The model requires layer wise soil data (physical and chemical), including soil texture and other soil properties. Daily weather data, including maximum and minimum air temperature (°C), solar radiation (MJ m−2 day−1) and precipitation (mm) (Fig. 1) were used as inputs. Data describing management

Table 1 – Genetic coefficients of CROPGRO-Peanut model for cultivar TMV 2. Coefficient CSDL

FSD-PHM PODDUR

PPSEN

WTPSD XFRUIT EMG-FLW SDPDVR

THRESH

FLW-FSD SDEDUR

SDPRO SDLIP

Definition Critical short day length below which reproductive development progresses with no day length effect (for short-day plants) Time between first seed (R5) and physiological maturity (R7) Time required for cultivar to reach final pod load under optimal conditions (photo thermal days) Slope of the relative response of development to photoperiod with time (positive for short-day plants) (1 per hour) Maximum weight per seed (g) Maximum fraction of daily growth that is partitioned to (seed + shell) Time between plant emergence and flower appearance (R1) (photo thermal days) Average seed per pod under standard growing conditions (number of seeds per pod) The maximum ratio of [seed/(seed + shell)] at maturity. Causes seed to stop growing as their dry weights increase until shells are filled in a cohort (threshing percentage). Time between first flower and first seed (R5) Seed-filling duration for pod cohort under standard growth conditions (photo thermal days) Fraction protein in seeds [g(protein)/ g(seed)] Fraction oil in seeds [g(oil)/g(seed)]

TMV-2 11.84

62.00 15.00

0.00

practices and information about cultivar-specific genetic coefficients [8] were used to calibrate the model. The genotype data file contains genetic coefficient data, namely the genetic coefficient, which describe specific cultivar characteristics of peanut. The CROPGRO-Peanut model uses 15 genetic coefficients to define development and growth characteristics of a peanut cultivar [7].

2.2.2. Model calibration The calibration of the CROPGRO-Peanut model was based on data from end-of-season sampling of yield and yield components of the 2012 field experiment. The genetic coefficients of the peanut cultivar TMV-2 that affect the phenological stages in the CROPGRO models were derived using the “trial-and-error” method [19] of DSSAT v 4.5. Adjustment was performed to match the observed crop phenology and yield with the simulated values and to make the calibrated genetic coefficient lie within the predefined error limits for the TMV 2 cultivar. Following this method, all coefficients were optimized for further simulation (Table 1). For calibration, information for key phenological events (anthesis day, first pod day, first seed day), and yield-related data including pod yield, LAI, HI, and shelling % were used. The combination of genetic parameters giving the minimum error was selected. The measured values of soil albedo,

0.440 0.920 17.40 1.78

Table 2 – Calibration results of CROPGRO-Peanut model using the data set from the four sowing dates in combination with no fertilization during 2012 (mean error a). Parameters

78.00

17.50 26.00

0.270 0.510

Sowing dates with no fertilizer January January February February 14 29 14 28

Anthesis day First pod day First seed day Pod yield at harvest (t ha−1) HI Shelling (%) a

0 0.04 0 0.11

0 −0.06 −0.13 0.07

−0.03 −0.04 −0.04 −0.05

0 −0.02 0 −0.06

−0.02 −0.01

−0.09 −0.03

−0.16 0.03

−0.24 0.31

Mean error = (simulated-observed)/observed.

Please cite this article as: D. Halder, et al., Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut..., The Crop Journal (2017), http://dx.doi.org/10.1016/j.cj.2017.02.005

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Table 3 – Evaluation results for CROPCRO-Peanut simulations of pod yield and aboveground biomass at harvest for the calibration and validation conditions. Year

Yield parameter

2012

Calibration Pod yield (t ha−1) ADM (t ha−1) Validation Pod yield (t ha−1) ADM (t ha−1)

2013

Number of treatment

Xobs

Xsim

α

16 16

2.029 5.908

2.074 5.769

0.881 0.988

0.364 0.206

16 16

2.025 4.562

2.279 5.391

0.498 0.951

0.888 −0.520

drainage rate, root growth factor, etc. for the sandy loam soil were not available for use as input to the model. The model simulation was accordingly started with the default values available in the model for similar soils of other regions.

RMSEa (t ha− 1)

RMSEn (%)

0.777 0.757

0.474 1.081

23 18

0.729 0.906

1.244 3.555

61 78

as the model parameters were not calibrated on the basis of the 2013 dataset. All the simulated and observed yield and yield components were compared and presented graphically. Statistical analysis was performed based on summary measures, which describe the quality of simulation, and on difference measures, which quantify errors. Summary measures were the mean of measured and simulated values, standard deviation of observed and simulated values, and the slope α, intercept β and coefficient of determination R2 of the

2.2.3. Model evaluation strategy The performance of the CROPGRO-Peanut model was evaluated using the dataset from the 2013 field experiments. This approach can be considered as a true validation of the model,

(a)

R2

β

3.0

2012

Simulated

2.5 2.0 1.5 1.0

R2 = 0.7770

R2 = 0.7288

0.5 0.0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

(b)

R2 = 0.7153

(c)

7.0

8.0

6.0

7.0 6.0

Simulated

5.0

5.0

4.0

4.0 3.0 3.0 2.0

R2 = 0.7567

2.0

1.0

R² = 0.9060

1.0

0.0

0.0 0.0

1.0

2.0

3.0

4.0

Observed

5.0

6.0

7.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Observed

Fig. 2 – Evaluation of CROPGRO-Peanut model for final pod yield, pod number at harvest and aboveground dry matter. (a) Pod yield at harvest (t ha−1); (b) pod number at harvest (number m− 2); (c) aboveground dry matter (t ha−1). Please cite this article as: D. Halder, et al., Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut..., The Crop Journal (2017), http://dx.doi.org/10.1016/j.cj.2017.02.005

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(a) Simulated pod yield (t ha-1)

3.0

R2= 0.722 RMSEa=0.114 (t ha-1) RMSEn=5.633 (%)

2.5 2.0 1.5 1.0 0.5 0.0 0.0

0.5

1.0

1.5

2.0

2.5

3.0

-1

Simulated aboveground dry matter (t ha-1)

Observed pod yield (t ha )

(b)

8.0 7.0

R2= 0.930 RMSEa=0.243 (t ha-1) RMSEn=4.655 (%)

6.0 5.0 4.0 3.0 2.0 1.0 0.0 0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

-1

Observed aboveground dry matter (t ha ) Fig. 3 – Simulated versus measured (a) pod yield and (b) aboveground dry matter (t ha− 1) from data set 2012–2013.

linear regression line between simulated and observed values. The absolute root mean square error (RMSEa) and normalized root mean square error (RMSEn) were calculated as follows:  RMSEa ¼

1 n ∑ ðS –O Þ2 N i¼1 i i

where Si and Oi are simulated and measured values, respectively. For good model performance the RMSE values should be as close as possible to 0. A model reproduces experimental values perfectly when α is 1, β is 0, and R2 is 1.

0:5

RMSEn ¼ ðRMSEa =mean of all measured valuesÞ  100

ð1Þ

2.2.4. Sensitivity analysis

ð2Þ

Sensitivity analysis was performed to assess changes in model output values relative to changes in model input values. First the values of all the input variables were set.

Table 4 – Simulation of effect of different sowing dates on yield and yield parameters of peanut (without fertilizer). Date of sowing

Yield at maturity (t ha−1)

Aboveground dry matter (t ha− 1)

Number of pods at maturity (number m−2)

Harvest index

Shelling (%)

1st January 7th January 14th January 21st January 29th January 7th February 14th February 21th February 28th February CV%

1.158 1.319 2.312 1.420 2.292 1.942 1.756 1.948 1.948 23.03

6.518 6.831 4.935 7.088 5.055 8.090 4.292 7.372 3.658 23.64

641 667 979 684 973 824 776 879 787 15.66

0.178 0.193 0.326 0.200 0.319 0.240 0.312 0.264 0.315 23.00

57.150 59.880 71.730 61.720 71.700 67.030 70.670 70.700 68.150 8.34

Please cite this article as: D. Halder, et al., Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut..., The Crop Journal (2017), http://dx.doi.org/10.1016/j.cj.2017.02.005

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(a) 3.0 -1

2.5

Yield at maturity (t ha ) 10 kg P O ha 2

Yield (t ha-1)

2

2.0

Yield at maturity (t ha ) 40 kg P O ha 2

Yield at maturity (t ha ) 60 kg P O ha 2

-1

5

-1

Yield at maturity (t ha ) 80 kg P O ha 2

-1

5

-1

1.0

-1

5

-1

Yield at maturity (t ha ) 50 kg P O ha

1.5

-1

5

-1

2

-1

5

-1

Yield at maturity (t ha ) 30 kg P O ha

-1

5

0.5 0.0

1st 7th 14th 21st 29th 7th 14th 21th 28th January January January January January February February February February

(b) -1

ADM at maturity (t ha ) 10 kg P O ha 2

-1

5

-1

ADM at maturity (t ha ) 30 kg P O ha 2

-1

5

-1

ADM at maturity (t ha ) 40 kg P O ha 2

-1

5

-1

ADM at maturity (t ha ) 50 kg P O ha 2

-1

5

-1

ADM at maturity (t ha ) 60 kg P O ha 2

-1

5

-1

ADM at maturity (t ha ) 80 kg P O ha 2

-1

5

(c) -2

Numbers at maturity (number m ) -1 10 kg P O ha 2 5 -2 Numbers at maturity (number m ) -1 30 kg P O ha 2 5 -2 Numbers at maturity (number m ) -1 40 kg P O ha 2 5 -2 Numbers at maturity (number m ) -1 50 kg P O ha 2 5 -2 Numbers at maturity (number m ) -1 60 kg P O ha 2 5 -2 Numbers at maturity (number m ) -1 80 kg P O ha 2

(d)

5

0.35

Harvest Index

0.30 0.25 Harvest index at 10kg P O ha

-1

0.20

Harvest index at 30kg P O ha

-1 -1

0.15

Harvest index at 40kg P O ha Harvest index at 50kg P O ha

-1

Harvest index at 60kg P O ha

-1

Harvest index at 80kg P O ha

-1

2 2 2 2

0.10

2

0.05

2

5 5 5 5 5 5

0.00

1st 7th 14th 21st 29th 7th 14th 21th 28th January January January January January February February February February

(e)

80

Shelling (%)

60 Shelling % at 10kg P2O5ha-1

Shelling % at 30kg P2O5ha-1

40

Shelling % at 40kg P2O5ha-1 Shelling % at 50kg P2O5ha-1 Shelling % at 60kg P2O5ha-1

20

Shelling % at 80kg P2O5ha-1

0 1st 7th 14th 21st 29th 7th 14th 21th 28th January January January January January February February February February

Sowing date

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Then the values were changed within a specified range and the effect on output values with the increase or decrease of values of an input parameter was noted. This procedure revealed the sensitivity of the model to specific parameters, the trend of simulation values, and performance of the most sensitive parameter of the model. A coefficient of variation (CV%) was also calculated to assess the variability of output values. Coefficient of variation ðCV%Þ ¼ ðstandard deviation=meanÞ  100

ð3Þ

7

0.715 and 0.680 and of 0.757 and 0.906 respectively, showing accurate prediction by the model during the 2012 and 2013 cropping seasons. Simulation studies by Anothai et al. [9] showed that simulated pod yield at harvest, pods per plant, and aboveground dry matter at harvest were in good agreement with observed values. For pod yield, the value for R2 was high, ranging from 0.71 to 0.87. The pods per plant at harvest had R2 values that ranged from 0.70 to 0.87, whereas harvested aboveground dry matter had R2 values ranging from 0.55 to 0.85.

3.2.2. Performance of the model

3. Results and discussion 3.1. Calibration The calibrated genetic coefficients of TMV-2 derived from the CROPGRO-Peanut model are presented in Table 1. The model error (%) of different physical parameters for four sowing dates and without application of phosphorus fertilizer are presented in Table 2. The values of simulated and observed anthesis day, first seed day, first pod day, pod yield at harvest (t ha− 1), HI, and shelling % showed reasonable agreement during the 2012 growing season. Table 3 presents goodness-of-fit parameters for pod yield and aboveground dry matter (ADM). The model simulated ADM have high RMSEa and RMSEn values, three times greater than the observed values. For ADM, the high value of slope α and low value of intercept β indicate good estimation of the simulated values. The relatively high value of R2 indicates low spread of the data points over the linear line. For pod yield, α was lower than the ADM but β was much higher. Similarly, the much higher value of R2 indicates a low spread of data points. The model shows quite satisfactory results for ADM.

3.2. Model evaluation 3.2.1. Yield and yield parameters The observed and model simulated yield and yield parameters for 16 treatments during the 2012 and 2013 summer season are compared and presented in Fig. 2(a–c). The figure shows that during the calibration year (2012) the observed and simulated pod yields ranged from 1.6 to 2.5 t ha−1, with the R2 value 0.777, whereas during the validation period these yields ranged from 1.7 to 2.8 t ha−1 with an R2 value of 0.729. The distribution of the observed and simulated pod yield around the 1:1 line revealed both overestimation and underestimation during the calibration period; however, overestimation was done during the validation period by the model. Moreover, the trends for the observed and simulated pod yield were similar because of the gradual increase in optimum temperature. Similar results can be seen for other yield parameters: pod number at harvest (number m− 2) and aboveground dry matter, with R2 values of

Fig. 3 compares simulated with measured total pod yield and aboveground dry matter (Fig. 3-a, b) across all calibration and validation data. The RMSEa was 0.114 t ha−1 and RMSEn was 5.633% for pod yields and RMSEa of 0.243 t ha−1 and RMSEn 4.655% for aboveground dry matter. These results showed that the model was able to reasonably simulate pod yield and final biomass for different sowing-date and fertilizer combinations with low RMSEa and RMSEn and R2 > 0.7. According to Wallach and Goffinet [5], the RMSE value is used to test the agreement between simulated and observed data. A low RMSE value is always desirable. In contrast, the RMSEn value measures the relative difference between simulated and observed values. A simulation is considered excellent if the RMSEn value is < 10%: good if 20 < RMSEn > 10, and fair if 30 < RMSEn > 20 [13]. This result shows that the model performed well in simulating crop yield with a wide range of sowing date and fertilizer combinations. Similar results were reported by Putto et al. [2] and Pandey et al. [35].

3.3. Sensitivity analysis: determination of appropriate combination of sowing date and phosphorus fertilization level Sensitivity analysis of CROPGRO-Peanut was performed for summer peanut crops for the study area. The changes in the prediction of pod yield, aboveground dry matter at maturity, pod number at maturity, harvest index (HI) and shelling % of peanut with variation in sowing dates and phosphorus fertilization levels are presented in Table 4 and Fig. 4(a–e). The variation in predicted yield shows that sowing dates and fertilization levels are sensitive parameters. There was very little variation in predicted dates of anthesis day, first pod day, and first seed day with changes in sowing date (without phosphorus fertilization), as shown in Table 4. The predicted phenological parameters with different sowing dates at seven-day intervals reveal the sensitivity of the model to the date of sowing (Table 4). The results of the sensitivity study show that the highest pod yield (2.98 t ha−1) was obtained with 14th January sowing dates and 30 kg P2O5 ha− 1 of phosphorus fertilization, followed by 2.96 t ha−1 with 50 kg P2O5 and 14th January sowing, and 2.95 t ha−1 with 40 kg P2O5 and 28th February sowing. The highest aboveground dry matter at maturity (9.53 t ha−1) was

Fig. 4 – Effect of different sowing dates in combination with different phosphorus fertilization levels on yield and yield parameters of peanut. Please cite this article as: D. Halder, et al., Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut..., The Crop Journal (2017), http://dx.doi.org/10.1016/j.cj.2017.02.005

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obtained with 21st February sowing and 50 kg P2O5 ha−1 followed by 9.51 t ha− 1 with 21st February sowing and 30 kg P2O5 ha− 1 and 9.30 t ha− 1 with 21st February sowing and 60 kg P2O5 ha− 1. Harvest index (0.332) and threshing % (73.01%) obtained with 30 kg P2O5 ha− 1 and 14th January sowing and 40 kg P2O5 ha− 1 and 29th February sowing date. All the results showed that the maximum yield was obtained with the application of 30–50 kg P2O5 ha− 1 and a sowing date varying between January 14 and February 21. After that period, yields declined gradually with increasing fertilization level (80 kg P2O5 ha− 1), following the law of diminishing returns. This law states that in all productive processes, adding more of one factor of production, while holding all others constant, will at some point yield lower incremental per-unit returns [33]. According to Paul and William [33], the use of fertilizer improves crop production on farms and in gardens, but at some point, adding more fertilizer improves yield by less per unit of fertilizer and excessive quantities can even reduce the yield. It is moreover not economically viable, as found in the case of the 80 kg P2O5 ha−1 fertilization level. The model predicted that sowing between January 14 to February 21 results higher yield and yield components than very early (January 7) or late (February 28) sowing. The model prediction was then confirmed by the experimental results reported by Halder and Panda [4]. Use of computer simulation has become a rewarding experience for researchers and decision makers.

4. Conclusions Validation of the CROPGRO-Peanut model, with respect to low RMSEa and low RMSEn as well as R2 values for all the treatments, showed a good match between observed and simulated values of yields and yield parameters. Sensitivity analysis also revealed that the model is highly sensitive to variation in sowing date and phosphorus fertilization level. It may be concluded that in eastern Indian regions, sowing of peanut between the second week of January and the end of February, using 30–50 kg P2O5 ha−1 during the summer season, is profitable.

Acknowledgments The authors are thankful to the Agricultural and Food Engineering Department of the Indian Institute of Technology Kharagpur, India for providing facilities to conduct experiments. The authors acknowledge the India Meteorological Department, India for installing an automatic weather station at the institute.

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Please cite this article as: D. Halder, et al., Evaluation of the CROPGRO-Peanut model in simulating appropriate sowing date and phosphorus fertilizer application rate for peanut..., The Crop Journal (2017), http://dx.doi.org/10.1016/j.cj.2017.02.005