Performance evaluation of AquaCrop in simulating soil water storage, yield, and water productivity of rainfed soybeans (Glycine max L. merr) in Ile-Ife, Nigeria

Agricultural Water Management 213 (2019) 1130–1146

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Performance evaluation of AquaCrop in simulating soil water storage, yield, and water productivity of rainfed soybeans (Glycine max L. merr) in Ile-Ife, Nigeria

T

Omotayo B. Adeboyea, , Bart Schultzb, Kenneth O. Adekalua, Krishna C. Prasadc ⁎

a

Department of Agricultural and Environmental Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria Prof. em. of Land and Water Development, IHE Delft, the Netherlands c Freelance Irrigation and Water Resources Management Consultant, Lalitpur, Nepal b

ARTICLE INFO

ABSTRACT

Keywords: Soybean AquaCrop Crop modelling Water productivity Water conservation practices

In this study, experiments were carried out for two seasons at the teaching and research farms, Obafemi Awolowo, Nigeria to evaluate the performance of FAO AquaCrop version 5.0 in simulating canopy cover, soil water storage, above ground biomass, evapotranspiration, grain yield and water productivity of soybeans (TGX 1448 2E) under rainfed conditions. The Experimental treatments consisted of conventional and six water conservation practices: Tied ridges; Mulching; Bund; Tied ridges plus mulch; Tied ridges plus bund and Bund plus mulch. The experimental treatments were arranged in randomised complete block design with four replicates. The 2012 and 2011 data sets were used for calibration and validation respectively. The performance of AquaCrop was tested using the regression coefficient (b), coefficient of determination (R2), Root mean square error (RMSE), Normalised root mean square error (NRMSE), Nash and Sutcliffe efficiency coefficient (EF), Willmott’s index of agreement (d-index) and percentage bias (PBIAS). AquaCrop simulated canopy cover adequately for the two seasons, 0.95 ≤ b ≤ 0.99; R2 ≥ 0.95 and RMSE ≤ 11.6%. The model captured well the variability in soil water storage with 0.95 ≤ b ≤ 1.01; R2 ≥ 0.74 and RMSE ≤ 10.2 mm. AquaCrop simulated evapotranspiration with R2 = 0.66 and RMSE = 64.6 mm. The model simulated dry above ground biomass with RMSE ≤ 0.25 t ha−1 and d-index ≥ 0.98. Grain yield was simulated with b = 1.01; R2 = 0.99; RMSE of 0.03 t ha−1 d-index = 1.00 and the maximum deviation between the simulated and predicted grain yields is 3%. The water productivity was simulated with R2 = 0.78 and RMSE = 0.08 kg m-3. Considering the overall performance, the AquaCrop model is adequate in simulating canopy cover, dry above ground biomass and grain yield. However, it has low aptitude in simulating evapotranspiration and water productivity of rainfed soybeans under soil surfaces management practices.

1. Introduction Over the past four decades, many crop and environment simulation models were developed in order to investigate the current and forecasted effects of environmental and management factors on production of crops. Every crop growth model has equations that estimates biomass production from the captured resources for instance water, solar radiation, and carbon (iv) oxide (Alam et al., 1994). There are three main types of crop growth models and these are carbon-driven; radiationdriven and water-driven (Steduto, 2003). Carbon-driven models express growth and development of crops as a function of assimilated carbon and intercepted radiation, for instance (CROPGROwth) CROPGRO (Bhatia et al., 2008; Battisti et al., 2017) and (WOFOST) WOrld FOod STudies (Todorovic et al., 2009). Radiation-driven models obtain the

⁎

biomass of crops based on the captured solar radiation using a conversion coefficient called radiation use efficiency. Examples are Simulator mulTIdisciplinary for Crop Standard (STICS) (Brisson et al., 2003); Environmental Policy Integrated Climate (EPIC) (Ko et al., 2009) and Crop Environment REsources Synthesis (CERES) for simulating maize productivity (DeJonge et al., 2012). Water-driven models are based on the principle that the biomass produced is directly proportional to transpiration through a parameter called water productivity (WP) (Steduto and Albrizio, 2005). Water driven models have a wider application in time and space than the solar driven models because they normalize the WP for evaporation and carbon (iv) oxide concentration (Steduto et al., 2007; Hsiao et al., 2007). An example of water driven models is CropSyst, a model for simulating effects of cropping system management on environment and productivity (Stöckle et al., 2003).

Corresponding author.

https://doi.org/10.1016/j.agwat.2018.11.006 Received 3 May 2018; Received in revised form 6 November 2018; Accepted 8 November 2018 Available online 10 January 2019 0378-3774/ © 2018 Elsevier B.V. All rights reserved.

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Fig. 1. Location of the experimental field in Ile-Ife, Nigeria, West Africa.

SIMDualKc coupled with phasic Stewart’s water-yield model is a soil water balance model (Rosa et al., 2012; Paredes et al., 2014b). Other complex models that integrate individual and crop specific models are for example CROPGRO (grain legumes), CERES (maize), CERES-Rice, SUBSTOR (potato) (Singh et al., 1998). CASSAVA (cassava) include the Decision Support System for Agrotechnology (DSSAT) (Jones et al., 2003) and Agricultural Production Systems Simulator (APSIM) which contains interconnected models (Keating et al., 2003; Holzworth et al., 2014; Gaydon et al., 2017) and the CROPSIM-CERES (wheat and barley), the CANEGRO (sugarcane) (Marin et al., 2011). Crops are complex heterogeneous systems, difficult to be manipulated during experiments. Models that are user-friendly can be used for managing resources, teaching, and research. Most of the existing models are sophisticated; require advanced scientific and computational skills and large input and parameters that are not easily obtainable for many crops and environments, they need to be determined during experiments. Therefore, they are mechanistic and suitable for scientific research and analysis of systems only. End-users of the models such as consulting engineers, farmer associations, extension practitioners, governmental agencies, non-governmental organization (NGO) face huge challenges in using the models thereby restricting their usage to scientists. Therefore, complex models could have higher reliability

but needs advanced skills than simple and robust crop yield models (Sinclair and Seligman, 1996). In order to address these challenges, and ensure robustness, accuracy and simplicity, the Food and Agriculture Organization of the United Nations (FAO) developed the AquaCrop model that requires relatively fewer explicit parameters and largelyintuitive input variables, that are either generally available or requiring simple procedures for their computation (Steduto et al., 2009). AquaCrop originated from the concept of crop yield response to water availability that was developed by Doorenbos et al. (1979). AquaCrop has already been calibrated for many vegetative, fruit, grain, oil, root, and herbaceous plants (Steduto et al., 2012). Despite the straightforward approach in AquaCrop, it is based on basic and complex biophysical processes in order to quantitatively assess effects of environmental factors on crop productivity (Hsiao et al., 2009). AquaCrop simulates growth and development of crops in calendar days or thermal time-growing degree-days (GDD). AquaCrop has been used to simulate yield and WP of many herbaceous crops for instance: cotton (Gossypium hirsutum L.) (Farahani et al., 2009); maize (Paredes et al., 2014a; Akumaga et al., 2017); rice (Maniruzzaman et al., 2015); soybeans (Paredes et al., 2015; Gimenez et al., 2017; Adeboye et al., 2017a); wheat (Triticum aestivum L) (Toumi et al., 2016) and sorghum (Araya et al., 2016). Adeboye et al. (2017a) showed that AquaCrop gave good 1131

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prediction of canopy cover (0.96 ≤ coefficient of determination (R2 ≤ 0.98); 4.3 ≤ Root mean square error (RMSE) ≤ 5.9 and 0.96 ≤ Willmott’s index of agreement (d-index) ≥ 0.99); dry above ground biomass (0.96 ≤ R2 ≤ 0.99; 0.06 ≤ RMSE ≤ 0.33 and 0.88 ≤ d-index ≥ 0.99); soil water storage (SWS) (0.75 ≤ R2 ≤ 0.95; 11 ≤ RMSE ≤ 18.1 and 0.78 ≤ d-index ≥ 0.99); grain yield (R2 = 0.99; RMSE = 0.1 t ha−1; d-index = 0.99) and water productivity (WP) (R2 = 0.58; RMSE = 2.03 kg ha-1 mm−1; d-index = -0.23) for soybeans. They recommended the model for managing water resources under deficit irrigated farming system. Battisti and Sentelhas (2015) evaluated the performance of several cultivars of the grain yield of soybeans using AquaCrop and reported that the model performed well with bias and mean absolute errors of 7 kg ha−1 and 284 kg ha−1 respectively with dindex of 0.87. They concluded that the crop yield model is an efficient tool for identifying soybeans cultivars that are tolerant to drought in Brazil. There is a need to manage seasonal rainfall at field level to improve land and water productivity of crops. One of promising measures is the conservation of water for crops that can be greatly affected by water stress during sensitive stages of their growth. Under rainfed agriculture, fluctuations in rainfall may subject crops to water stress and consequently reduce yield. Soybean is an economic crop in the sub-humid and savannah agro-ecologic zones of Nigeria. The performance of the AquaCrop model in simulating soil water and crop parameters for rainfed soybean under water conservation practices has not been evaluated. Therefore, the objectives of this study consisted in evaluating the performance of AquaCrop in simulating canopy cover (CC), soil water storage (SWS), evapotranspiration (ET), dry above ground biomass (DAB), grain yield, and water productivity (WP) of rainfed soybeans under the conventional and six water conservation practices in Ile-Ife, Nigeria.

to October) within the past twenty eight years in the area. Rainfall during the reproductive stage in August, 2011 was 20% of the seasonal rainfall unlike in 2012 when the comparative rainfall was 34%. The recession in rainfall commonly called the August break in the area was higher in 2012 than in 2011. In the first season, only 10% of the rainfall occurred during the vegetative (VE – V2) and reproductive (R3 – R6) stages while 16% occurred during seed maturity (R7 – R8). In the second season, about 38 and 11% of the rainfall events occurred during the (VE – V2) and reproductive stages, respectively. The mean solar radiation in 2011 and 2012 were 93.4 and 85.1 MJ m-2 day−1 respectively while the comparative average seasonal values were 143 and 132 MJ m-2 day−1. The daily weather data in the seasons were imputed into the climate component of the model for the simulations.

2. Materials and methods

2.4.1. Experimental treatments The treatments consisted of a conventional practice (NC) of direct planting of the crop on bare land without a conservation practice and six water conservation practices:

2.3. Soil properties Soil samples were obtained to a depth of 100 cm using 53 mm core soil sampler (Eijkelkamp, Netherlands) from three locations on the field and the saturated hydraulic conductivity (Ksat) of each layer was determined using Guelph permeameter (Fares et al., 2000). The Ksat are within the range presented for sandy loam in (Raes et al., 2012) but higher than sandy clay loam due to differences in the locations. The upper 40 cm was characterized by sandy loam, while the lower 40–80 cm was sandy clay loam (Table 1). The bulk density was lower in the upper 60 cm than the lower 60 to 100 cm. Organic matter was higher in the upper 60 cm than the lower 100 cm, due to accumulated plant material over the years. Soil pH in the upper 60 cm is adequate for the cultivation of Soybeans. The soil profile characteristics were imputed into the soil component of AquaCrop for the simulation. 2.4. Field layout, cultivation practice, and measurement

2.1. Study area The experiments were carried out during the rainy seasons of 2011 and 2012 at the Teaching and Research Farms of Obafemi Awolowo University, Ile-Ife, Nigeria, latitude 7° 33′ 0′'N and longitude 4° 34′ 0′'E, 271 m + MSL (mean sea level) (Fig. 1). Ile-Ife is located in Ogun-Osun River Basin, Nigeria.

• Tied ridges (TR). The 6 m long ridges were constructed manually •

2.2. Meteorological data and environmental condition Air temperature (°C) and relative humidity (%) were measured at intervals of 10 min using VISALA HMP45 while an ultrasonic anemometer (Campbell Scientific, USA) was used to measure wind speed (m s−1) at 2 m above soil surface. Pyranometers (Apogee Instruments, USA) was used to measure daily global solar radiation (MJ m-2 day−1). Average of each meteorological data for 24 h was taken as the daily value after data quality check. The instruments were installed at 300 m from the experimental field. Reference evapotranspiration (ETo) under standard conditions was determined using the Penman-Monteith approach (Allen et al., 1998). Daily rainfall was measured manually from the three rain gauges that were sparsely positioned on the experimental field. The daily air temperatures, relative humidity, rainfall and ETo in 2011 and 2012 are shown in Figs. 2 and 3 respectively. The maximum and minimum air temperatures in 2011 (May to September) were 32.3 and 19.9 °C respectively while the comparative temperatures for 2012 (June to October) were 32.8 and 19.8 °C. The peak air temperature from May to October in the study area during the past twenty-eight (28) years was 35.2 °C while the minimum was 23.6 °C. The seasonal air temperature in the 2011 (May to September) was higher than that of 2012 (June to October) and 2012 was more humid than the 2011. The seasonal rainfall was 539 and 761 mm in the years 2011 and 2012, respectively and lower than the peak seasonal rainfall of 981 mm (May

• • • •

with hand hoe used by the local farmers in Ile-Ife. The ties were 0.6 m high and spaced at intervals of 1.5 m along the row before planting. This was done to eliminate surface runoff. The ties were reinforced after weeding during the seasons; Mulching (ML). Panicum maximum, 0.2 m thick layer was spread on the ridges at 21.6 t ha-1 and covered 75% of the soil surface after the crop establishment. Mulches were reinforced after weeding and applied three times before maturity; Bund (BD). Side bunds 60 cm high and 30 cm thick were constructed manually around the plot such that it forms a micro-catchment, eliminating surface runoff and concentrating the trapped rainfall within the root zone; Tied ridges plus mulch (TRML). A combination of TR and ML; Tied ridges plus bund (TRBD). A combination of TR and BD; Bund plus mulch (MLBD). A combination of ML and BD.

Detailed diagrams of the practices and descriptions of their effects on SWS, yields and other parameters can be found in Adeboye et al. (2017b). The year 2012 was wetter and therefore the field data were used for the calibration of the model while the field data set for 2011 was used for validation. 2.4.2. Cultivation practices and measurements The treatments were laid out in a randomized complete block design with four replicates after ploughing and harrowing. Indeterminate variety of soybeans TGX 1448 2E, was planted on May 24 Day of the year (DOY 144) in 2011and June 15 (DOY 167) in 2012. Each plot was 6 m by 6 m (36 m2) and plant spacing was 0.3 by 0.6 m. Four seeds were 1132

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Fig. 2. Daily meteorological data on the experimental field for the year 2011.

Fig. 3. Daily meteorological data on the experimental field in the year 2012.

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CC = [1

Table 1 Physical and chemical properties of the soil at the experimental field in the growing seasons. Soil depth (cm)

00 - 20

20 - 40

40 - 60

60 -80

80 - 100

Sand (%) Clay (%) Silt (%) Texture class*

75 15 10 Sandy loam 1.49 1.28 0.18 0.08 0.10 880

71 19 10 Sandy loam 1.56 0.74 0.27 0.13 0.10 570

59 21 20 Sandy clay loam 1.58 0.61 0.2 0.09 0.12 415

57 30 13 Sandy clay loam 1.57 0.44 0.31 0.21 0.10 350

63 18 19 Sandy loam 1.62 0.34 0.29 0.19 0.10 580

−3

BD (g cm ) OM (%) FC (m3 m−3) PWP (m3 m−3) TAW (m3 m−3) Ksat (mm day−1)

ET = P + I + CR

2.4.3. Measurement of soil water Soil moisture sensors, 5 TE (Meter Groups, USA) were installed from 00 to 60 cm at intervals of 10 cm after calibration. The initial soil moisture content at 00–10 cm was measured immediately after sowing of the seeds. The soil water content was measured at intervals of seven day throughout the cropping seasons. The SWS in the stated depth was determined using the Eq. (1) (Liu et al., 2013):

ET = P

× zi

D± s

(3)

RO

DP ± s

(4)

Water productivity was determined from the ratio of the marketable grain yield to actual crop ET (Adeboye et al., 2017b): 2.5. Description of AquaCrop model

n i

RO

where: ET= actual crop evapotranspiration (mm) P= rainfall (mm) I= irrigation (mm) CR= capillary rise (mm) DP= deep percolation (mm) RO= surface runoff (mm) Δs= change in SWS (mm) Surface runoff was measured from ML and NC by installing rigid metallic drums within 0.716 m2 of the plots in the replicates. Surface runoff was channelled to a graduated drum and measured after each rainfall (Adeboye et al., 2017b). Surface runoff in other practices was considered negligible because the Soil bund and ridges were 60 cm high and no case of bund or ridge overflow. Deep percolation was determined from the soil moisture content measured periodically but it was assumed negligible for deeper depths beyond 100 cm. The contribution of the groundwater was ignored because the groundwater table was deeper than 60 m. There was no irrigation during the crop cycle. Change in the SWS was determined from the difference between water storage on the sampling dates and their respective preceding dates. Therefore, Eq. (3) is reduced to Eq. (4):

sown at a depth of 4 cm (Steduto et al., 2012) which produced 222,220 plants ha−1. Each plot was thinned to three plants per stand and mulches applied to the selected plots after full establishment. Phosphorus was applied in the form of single super phosphate fertilizer at 30 kg ha-1 (Dugje et al., 2009). The plots were separated by alleyways of 1 m and 2 m at the extreme boundaries of the fields. Ditches of 0.3 m deep, 0.3 m wide were constructed around the field to divert runoff from the adjacent plots. Insects were controlled with “Magic Force™” Lambda-Cyhalothrin 15 g/L + Dimethoate 300 g/L EC at 1.5 L ha−1.

i=1

(2)

2.4.6. Crop evapotranspiration The actual crop ET was determined by using the soil water balance approach in Eq. (3) (Chattaraj et al., 2013):

BD, Bulk density; FC, Field capacity; PWP, permanent wilting point; TAW, Total available water; OM, Organic matter; *USDA Classification.

SWS =

(PAR below / PARabove )] × 100

The FAO AquaCrop model is a crop growth model that simulates potential yield of herbaceous crops as a function of the amount of water transpired by the crop under different irrigation regimes and rainfed conditions (Steduto et al., 2012). AquaCrop is water-driven model because in the growth engine, transpiration is translated into biomass using conservative crop parameters, the biomass water productivity, normalized for atmospheric evaporative demand and air CO2 concentration (Steduto et al., 2012):

(1)

where: SWS= total soil water storage (mm) θi= moisture content for soil layer i (m3 m−3) z= soil depth or layer i (mm) n= number of soil layers within the root zone The measured SWS was compared with the simulated SWS.

n

Bn = WP *×

2.4.4. Above ground biomass At intervals of 2 weeks from 21 days after planting (DAP) in 2011 and 1 week from 28 DAP in 2012, the biomass were harvested from an area of 0.716 m2 randomly from the four replicates of each treatment and dried in oven at 70 °C (Memmert, Sweden) for 24–48 h to measure the dry weight. The dry above ground biomass (DAB) per unit area was estimated. Total of 11 and 13 consecutive measurements of the DAB were made in 2011 and 2012, respectively. From the ratio of the grain yield to the oven dried DAB, harvest index (HI) was determined (Siahpoosh and Dehghanian, 2012).

i=1

Tri EToi

(5)

where: Bn is the cumulative aboveground biomass production after n days (g m−2); Tri is the daily crop transpiration (mm day-1); EToi is the daily reference evapotranspiration (mm day-1); n are the sequential days of the period when biomass is produced; WP* is the normalized crop water productivity (g m−2). The purpose of normalization is to make AquaCrop applicable to many geographic areas, climates, and cropping seasons. Simulation in AquaCrop can be operated in two distinct modes, thermal or calendar time in daily time steps. AquaCrop uses canopy cover instead of localised leaf area index to estimate transpiration and to separate soil evaporation from transpiration. Crop yield is estimated as the product of final dry above-ground biomass and harvest index (Raes et al., 2009). AquaCrop does not partition biomass into various components. At the commencement of flowering, HI increases linearly with time after a lag phase, until near physiological maturity. AquaCrop requires local precipitation, minimum and maximum temperature and ETo to simulate daily crop growth and development. The crop canopy development and phenology are driven by temperature. Soil water storage depends upon the computations of above ground biomass and

2.4.5. Leaf area index and canopy cover At an average interval of 10 days from 18 DAP and 7 days from 28 DAP in the year 2011 and 2012, respectively, the photosynthetically active radiation (PAR), were measured using AccuPAR LP 80 (Meter Group, USA) near solar noon. From triplicates, ten samples of the below and above PARs were taken by placing the line sensor perpendicularly to the rows above and below the plant canopy and the average values displayed were recorded. Due to the importance of the canopy cover (CC) in AquaCrop, it was determined in the two seasons using Eq. (2) (Kamara et al., 2014): 1134

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yield. The model simulate responses of crop to water deficits using four coefficients that are functions of fractional available soil water regulated by evaporative demand of the atmosphere, using sensitivity to water stress of four key plant processes which are canopy expansion, stomata control of transpiration, canopy senescence, and harvest index. Harvest index can be adjusted depending on the level, duration and timing of the stress. AquaCrop uses relatively few parameters which are explicit and often intuitive. The model is designed for broad range of end-users such as governmental and non-governmental agencies, economists, water managers, extension officers, practicing engineers, and educated farmers. It is also designed to meet the need of economists and specialists in policy who use simple crop yield models for planning and analysis of effects of future climate scenarios on management of water for agriculture at field and basin scales. It is also designed as a research tools for the analysis of environmental effects on crop growth and development (Steduto et al., 2009).

Table 3 Non-conservative parameters used in calibrating and validating the model for the simulating the response of Soybeans to the practices.

2.5.1. Conservative and non-conservative parameters The parameters for crops in AquaCrop are described as conservative and non-conservative. The conservative parameters do not change with time, location, management or cultivar (Raes et al., 2012). These conservative parameters include, canopy cover (CC); canopy growth coefficient (CGC); canopy decline coefficient (CDC); crop coefficient for transpiration at full CC; WP* for biomass formation; coefficients for adjusting the HI in relation to inhibition of leaf growth and stomata conductance; soil water depletion thresholds for inhibition of leaf growth and of stomata conductance and for the acceleration of canopy senescence (Table 2). The non-conservative parameters depend on environmental conditions, management decisions, and cultivars. They are generally specified by the users during simulation (Table 3). The nonconservative parameters were estimated using the data obtained during the 2011 and 2012 cropping seasons. Parametization of the CC is very important in AquaCrop because it influences transpiration, which has effects on the final grain yield of the crop. Leaf expansion growth is more sensitive to water stress than stomata conductance and canopy senescence (Steduto et al., 2009). AquaCrop was run in growing degree days (GDD) in the current study. We estimated the initial CC using the row and plant spacing in the model while measured CC were imputed in

Parameter/Treatment Label

Value

Unit

CGC CDC at senescence Crop coefficient for transpiration at CC = 100% *WP for ETo and CO2 Soil water depletion for lower canopy expansion Leaf growth stress coefficient curve shape Soil water depletion threshold for upper stomata control Stomata stress coefficient curve shape Shape factor for water stress coefficient for canopy senescence Maximum basal crop coefficient Kcb Time from sowing to emergence Time from sowing to start of flowering Duration of flowering Time from sowing to start of senescence Time from sowing to maturity Duration of building up of the HI Time from sowing to maximum effective rooting depth Maximum effective rooting depth

15.4 0.934 1.15

% per day per GDD Full canopy transpiration relative to ETo g m−2, (biomass) Leaf growth stops completely at this value Moderately convex shape

17.6 0.65 3.3 0.58 3.5 3.2

Above this stomata begins to close Highly convex curve Convex curve

1.14 166 841 315 2044 2433 1426 1820

GDD GDD GDD GDD GDD GDD GDD

0.6

m

the data file for simulations. After imputing the dates of 90% emergence, and physiological maturity, the rates of canopy expansion were generated automatically. The grain yield was simulated by imputing date and duration of flowering, reference harvest index (HIo) and duration of building up of harvest into the model. The effects of six water conservation and the conventional practices on the yield components and SWS were simulated in the field management sub-component of the model. Under the field management practices, prevention of surface runoff was selected for Tied ridges and soil bund and therefore there was no curve number for the computation of surface runoff. Under mulching, organic plant material which reduced evaporation

Table 2 Conservative parameters in simulating the response of Soybeans (Raes et al., 2012). Description Crop phenology Base temperature Cut-off temperature Canopy cover per seedling at 90% emergence (CCo) Canopy growth coefficient (CGC) Minimum effective rooting depth Maximum effective rooting depth Shape factor for root zone expansion Effect of CC on soil evaporation reduction at late season Decline in crop coefficient after reaching CCx Canopy decline coefficient at senescence (CDC) Crop coefficient for transpiration (KcTrx) at CC = 100% Water productivity normalized for ETo and CO2 Water productivity normalized for ETo and CO2 during yield formation (%WP* before yield formation) Reference harvest index Soil water depletion threshold for canopy expansion-Upper Allowable maximum increase (%) of specified HI Soil water depletion threshold for canopy expansion-lower Leaf growth stress coefficient curve shape Soil water depletion threshold for stomata control - Upper threshold Stomata stress coefficient curve shape Soil water depletion threshold for canopy senescence (Psen) - Upper threshold Shape factor for Water stress coefficient for canopy senescence Coefficient for positive impact of restricted vegetative growth during maturity on HI Coefficient describing negative impact of stomata closure during yield formation on HI Allowable maximum increase (%) of specified HI

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Value

Unit or meaning of symbol

5.0 30.0 0.28

o

0.3 2.4 1.5 25 0.30% 1.10 15.0 60 40 0.15 10 0.65 3.0 0.5 3.0 0.7 3.0 None Strong 10

C C cm2/plant Increase in CC relative to existing CC per day m m m – Decline per day due to leaf aging Decrease in CC relative to CC per GDD Full canopy transpiration relative to ETo g m−2, (biomass) o

% As fraction of TAW, above this leaf growth is inhibited Leaf growth stops completely as this p Moderately convex shape Above this stomata begins to close Highly convex curve Above this early canopy senescence begins Convex curve HI increased by inhibition of leaf growth at anthesis HI reduced by inhibition of stomata at anthesis –

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losses by 50% was selected. Straight row crop, good hydrologic conditions and hydrologic soil group A were selected and they increased the curve number by 20%. The field management practices for Tied ridge, mulch and soil bund were combined together for the simulation of the parameters for Tied ridge plus mulch, Tied ridge plus soil bund and Mulch plus soil bund accordingly. For the conventional practice, no parameter under the field management was selected during the simulation.

relative difference between the model and measured data set. It is expressed in Eq. (9):

NRMSE =

EF = 1

Evaluation of the performance of a model is an important step in its verification (Addiscott et al., 1995). This is done by comparing measured data and results of simulation created by AquaCrop using graphical illustrations (Loague and Green, 1991; ASCE, 1993) and statistical indices (Moriasi et al., 2007). In this study, grain yield, DAB, crop ET, SWS within root zone and WP, were considered in the model evaluation. The first method is the forcing of linear regression line that relates the measured and predicted values through the origin and regression coefficient (b) is used as indicators. A value of b close to 1.0 implies that the measured and predicted data are statistically close. It has been observed that single measure cannot be used to judge the performance of a model and therefore, many statistical indices should be used for evaluation (Gauch et al., 2003; Ma et al., 2012). In addition to b, other statistical indicators used for the evaluation are: Coefficient of determination (R2) which expresses the degree of collinearity between the measured and simulated data set is written as n i=1 n i=1

(Oi

O )(Pi

O )2

(Oi

n i=1

(Pi

P )2

d

Pd =

Mi Mi

× 100

(6)

n i=1

(Pi n

n (Pi i=1 n ( Oi i=1

index = 1

PBIAS =

(9)

Oi)2 (10)

O )2

n i=1

n i=1

(|Pi

(Pi

Oi )2

O | + |Oi

O |)2

(11)

n i=1

Oi )2 × 100

(Pi n i=1

(Oi )

(12)

The PBIAS has optimal value of 0.0, with low-magnitude values indicating accurate model simulation. Positive values indicate model underestimation bias, and negative values indicate model overestimation bias (Pereira et al., 2015). The PBIAS ranging from −15 to +15% is acceptable for evaluating crop simulation models (Ma et al., 2011) 3. Results and discussion 3.1. Model performance 3.1.1. Canopy cover As stated earlier, data from the field in 2012 was used for the calibration of the model while those of 2011 were used for the model validation. The initial canopy cover (CCo) for Tied ridge at 90% emergence and maximum canopy cover (CCx) were 0.28 and 99% respectively. The canopy growth coefficient (CGC) and canopy decline coefficient (CDC) were 15.4% per day and 0.934% GDD, respectively (Table 3). Crop coefficient for transpiration (KcTr,x), readily evaporable water (REW) for each treatment were adjusted after which the measured and simulated soil water and ET were simulated and compared with measured data. Soybean is a C3 plants in which a 3-carbon intermediate acid is the first stable product during CO2 fixation (Decoteau, 2005). The default normalized water productivity (WP*) is 15 g m−2. In order to improve the prediction in AquaCrop, WP* was increased to 17 g m−2 which falls within 15 to 20 g m−2 acceptable for C3 crops (Adeboye et al., 2017a). The harvestable yield of a crop is the product of HI and biomass accumulated over time. This conservative parameters need to be adjusted in order to improve the predicted values in the model (Hsiao et al., 2009; Paredes et al., 2015). In this study, the upper and lower values of the canopy expansion were used. The key parameters that were adjusted in the simulation are listed in Table 3. The graphical illustrations of the canopy cover are shown in Fig. 4. The goodness-of-fit statistical

(7)

where: Si and Mi are the predicted and measured values of the of the treatment data set. Pd ranges between zero and infinity and values close to zero indicates better agreement between the simulated and measured values. The simulated data set is acceptable if Pd does not exceed 15%, which is the tolerance error range for field agronomic study (Brisson et al., 2002) The root mean square error (RMSE) is expressed in Eq. (8):

RMSE =

× 100

It ranges between 0 and 1. Value of 1 indicates a perfect agreement between the simulated and measured data and 0, no agreement (Krause et al., 2005). Percentage bias (PBIAS): Percent bias measures the average tendency of the simulated data to be larger or smaller than their measured data (Gupta et al., 1999). It is expressed in Eq. (12) as

where: Oi= measured data O = mean of measured data Pi= simulated data P = average of simulated data. R2 ranges from 0 to 1. R2 close to 1 indicates a good agreement between measured and simulated data set and that most of the variance of the measured data set is explained by the model. In most studies, R2 > 0.80 is recommended for crop simulation studies (Ma et al., 2011). Percentage deviation (Pd) was also used to quantify the deviation of simulated data from the measured data set (Eq. (7).

Si

n

EF indicates how well the plot of measured versus simulated data set fits the 1:1 line. - ∞ ≤ EF ≤ 1.0 (1 inclusive), with EF = 1 being the optimal value. Values between 0.0 and 1.0 are generally taken as acceptable performance, whereas values less than 0 mean that the mean of measured data set is a better predictor than the simulated data set and is unacceptable performance (Moriasi et al., 2007). Willmott’s index of agreement (d-index) is expressed (Eq. (11)):

2

P)

Oi )2

(Pi

NRMSE that is less than 10% is considered excellent; 10–20 is good; 20–30 is fair and greater than 30% is poor (Jamieson et al., 1991). The Nash-Sutcliffe efficiency coefficient (EF) is expressed in Eq. (10):

2.6. Model evaluation

R2 =

n i=1

1 O

Oi )2 (8)

where: n= number of measurements taken from the same treatment on different dates over the seasons. RMSE ranges from 0 to +∞. The former indicates optimal and the latter poor model performance. A value of 15% is considered “good” and 20% is “satisfactory” for agricultural models. Hanson et al. (1999) recommended 15% error for biomass and grain yield. The normalized root mean square error (NRMSE) empresses the 1136

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Fig. 4. Measured and simulated canopy cover for rainfed soybeans in the year 2012 using the calibrated parameters.

test for the calibrated data set in 2012 indicated excellent agreement between the predicted and measured CC with R2 (0.95–0.98) and indicates that the model accounted for 95–98% of the variability in CC. The RMSE (6.0–11.6%); NRMSE (8.5–14.6%) are considered good for the model. The EF (0.84–0.96) means that the variance of residuals was

much smaller than the variance of the measured data. The d-index (0.97-0.99) and PBIAS (-1.29-3.09) shows that the model is excellent in simulating CC for the crop under the soil conservation practices (Table 4). The validated data set in 2011 had high R2 (0.97–1.00) and low RMSE (5.0–9.0%); NRMSE (9.2–14.3); high EF (0.92–0.98) and d1137

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Table 4 Statistical indicators of the performance of AquaCrop in simulating canopy cover for rainfed soybeans in Ile-Ife, Nigeria. Year

Treatment Label

Regression Coefficient (b)

R2

RMSE (%)

NRMSE (%)

EF

d-index

PBIAS (%)

2011

TR BD ML TRML TRBD MLBD NC TR BD ML TRML TRBD MLBD NC

0.98 0.95 0.95 0.98 0.96 0.97 0.97 0.97 0.96 0.98 0.96 0.99 0.97 0.96

0.97 0.99 1.00 0.97 0.99 0.98 0.99 0.97 0.98 0.96 0.95 0.98 0.98 0.97

8.0 6.7 3.3 8.5 7.6 7.3 5.0 9.8 7.5 9.8 11.6 6.1 6.0 8.0

14.1 12.1 8.5 14.0 14.3 13.5 9.2 13.9 11.6 14.6 11.3 10.6 8.5 12.1

0.92 0.95 0.98 0.95 0.94 0.95 0.98 0.88 0.94 0.90 0.84 0.90 0.96 0.93

0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.98 0.99 0.98 0.97 0.98 0.99 0.98

−2.71 4.17 7.60 −0.02 −2.47 0.88 0.02 0.60 1.93 0.48 −0.92 −1.29 3.09 2.72

2012

index (0.98-0.99) and PBIAS (-2.47–7.60%). The calibrated and validated parameters in AquaCrop did not over estimate CC with b ≤ 0.99 under the water conservation and conventional practices. However, the model has the tendency to underestimate CC for Soil bund and Mulch and overestimate it for Tied ridge, Tied ridge plus mulch and Tied ridge plus soil bund as indicated in PBIAS in the validated data set (Table 4). The model did show little or no tendency to underestimate for Mulch plus soil bund and the conventional practice. AquaCrop simulated CC of soybeans with R2 > 0.91 and 4.3% ≤ RMSE ≤ 30.1% and −0.71 ≤ EF > 0.91 (Paredes et al., 2015). Considering the overall performance of the model in simulating CC when soil surface is managed to conserve water under rainfed conditions in the two seasons, the model can be judged good.

-0.28% ≤ PBIAS ≤ 5.63%) for the validated parameters (Table 5). AquaCrop overestimated SWS for Mulch, Tied ridge plus mulch and the conventional practices but underestimated SWS for other practices. Iqbal et al.(2014) observed similar trend of overestimated SWS by AquaCrop at the initial stage for wheat and found that the model underestimate SWS under severe water stress conditions. It has been found that AquaCrop overestimate SWS by a significant amount (Zeleke et al., 2011). AquaCrop underestimate SWS even below wilting point (Mkhabela and Bullock, 2012). Despite the over and under estimations of SWS, in 2011, estimation error is low and far below 15% recommended for agricultural models. Generally, the overall assessment of the model for calibrated and validated parameters shows that it passes the R2 and EF > 0.8 and 0.70 respectively suggested for agronomic simulations by Ma et al.(2011).

3.1.2. Soil water storage AquaCrop simulated SWS above field capacity at the early and late stages and almost near saturation for Tied ridge, Soil bund, Tied ridge plus Mulch and Mulch plus Soil bund for the calibrated data set (Fig. 5). There was water stress in all the treatments immediately after flowering, which could be attributed to low rainfall during this period compared with other stages of growth. AquaCrop did not simulate SWS below wilting point for the water conservation and conventional practices. This could be attributed to the availability of water within the root zone as indicated in the simulation parameters under the field management. The R2 for the calibrated data set was > 0.95 meaning that the model explained most of the variance in the SWS (Table 5). The RMSE < 10.2 mm and fall between 3.16–5.74% of the means of measured SWS and is considered very good for calibration in agricultural models (Ma et al., 2011). The NRMSE < 6.8% while EF > 0.8 and dindex > 0.83 which is acceptable for calibration (Saseendran et al., 2008). Similarly, the R2 > 0.74 with low RMSE and NRMSE for validated data set. The EF was positive and indicates that the residuals variance was lower than the measured data variance. The d-index > 0.86 was high, and shows mutual agreement between the measured and simulated SWS. The model did not over predict SWS with b ≤ 1.00. The model simulated SWS below field capacity at early (0–30 DAP) and late stages (95–117 DAP) for validated data set (Fig. 6). Soil water storages were near saturation in Soil bund, Tied ridge plus soil bund and Mulch plus soil bund because they were designed to eliminate surface runoff. The ‘goodness-of-fit’ indicators for the SWS shows that the model is not biased in the simulation of SWS. Coefficient of determination, R2 were relatively uniform (0.88-0.95) for the calibrated and (0.74–0.92) for the validated data sets. It indicates that the variability in the SWS was well captured in the model. There were overestimation and underestimation of the SWS for the simulation with calibrated parameters (0.96 ≤ b ≤ 1.00; −1.17% ≤ PBIAS ≤ 4.14%) and (0.95 ≤ b ≤ 1.00;

3.1.3. Crop evapotranspiration There was a general pattern of underestimation of ET when calibrated data set was used for the simulation (Table 6). The deviation (Pd) ranged between −20% for the Mulch plus soil bund to 2% for Mulch plot with the calibrated data set, and lower with the validated data set, −11% for Tied ridge plus soil bund to 36% for conventional flat planting. AquaCrop did not simulate ET well because deviation was above 15% recommended for crop yield models (Brisson et al., 2002). It could be attributed to its weakness in predicting transpiration and evaporation. There was low correlation, R2 = 0.66 (Fig. 7) between the measured and predicted ET for the pooled data which indicates that the model explain half of the variance in the simulated data set and is rated satisfactory as proposed by (Qi et al., 2011). The RMSE = 64.6 mm (14.1%) of the mean of measured SWS in our study and is within the acceptable limit for agronomic study. The EF = 0.62 and d-index = 0.99. Positive EF indicating that the residuals variance was lower than the measured data variance and high d-index shows close agreement between the measured and predicted SWS. Our results show that AquaCrop has a poor aptitude in simulating ET, which was also observed by Katerji et al. (2013). The model underestimated ET was for all the treatments in the calibrated data set except Mulch. Similarly, the model underestimated ET for Mulch, Tied ridge plus soil bund and Mulch plus soil bund in the validated data set. This may be due to an under-estimation of transpiration due to KcTr,x or under-estimation of soil evaporation since a 50% reduction was considered under mulch conditions. There were similar high deviations, 2.1–10.2% for ET of cotton (Gossypium hirsutum L) (Farahani et al., 2009) and −1.23% to −8.4% for maize (Zea mays L) by AquaCrop (Heng et al., 2009). 3.1.4. Biomass accumulation The deviations of the simulated biomass from the measured data in the calibrated data set was low (0.0–10%) while for the validated data 1138

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Fig. 5. Measured and simulated soil water storage for rainfed soybeans in the year 2012 using the calibrated parameters.

0.25 t ha−1, NRMSE (8.2 to 16.8%) while EF (0.93 to 0.98) and d-index (0.98 to 0.99) with PBIAS (2.25 to 13.25%) (Table 7). Similarly, for the validated data set, 0.99 ≤ R2 ≤ 1.0, RMSE (0.10 to 0.20 t ha−1), NRMSE (7.9 to 10.6%) while EF (0.97 to 0.99) and d-index (0.99 to 1.00) with PBIAS (−9.34 to 6.60%). There were cases of overestimation of dry biomass in validated data set with 0.93 ≤ b ≤ 1.10 indicating that AquaCrop have the tendency to overestimate biomass

set, it was (2–11%) (Table 6). The performance of the model in predicting biomass could be rated good because the deviation is lower than 15% recommended for agronomic studies (Brisson et al., 2003). The R2 for the simulated and measured above ground biomass ranged from 0.90 to 0.99 for the calibrated data set in 2012 and it indicates that the model accounted for 98 to 99% of the relationship between the simulated and measured biomass (Fig. 8). The RMSE ranged from 0.10 to 1139

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Table 5 Statistical indicators of the performance of AquaCrop in simulating soil water storage using the calibrated and validated parameters for soybeans in Ile-Ife, Nigeria. Year

Treatment Label

Regression Coefficient (b)

R2

RMSE (mm)

NRMSE (%)

EF

d-index

PBIAS (%)

2011

TR BD ML TRML TRBD MLBD NC TR BD ML TRML TRBD MLBD NC

0.96 0.99 1.01 1.00 0.99 0.98 0.99 0.95 0.99 0.97 0.99 0.99 0.97 1.00

0.80 0.80 0.80 0.74 0.80 0.80 0.92 0.88 0.93 0.95 0.88 0.89 0.92 0.89

10.1 7.0 9.8 6.7 4.2 7.0 4.6 10.2 5.8 9.5 6.7 8.4 8.5 6.9

9.1 4.0 5.3 3.7 2.3 3.9 2.6 6.8 3.1 5.7 3.6 4.5 4.7 3.8

0.53 0.63 0.53 0.48 0.60 0.50 0.82 0.49 0.85 0.80 0.76 0.77 0.66 0.75

0.86 0.96 0.96 0.93 0.93 0.92 0.94 0.83 0.87 0.83 0.83 0.87 0.84 0.94

4.14 0.50 −1.17 −0.49 0.90 2.30 0.62 5.03 0.27 2.76 0.92 1.48 5.62 −0.28

2012

for Tied ridge, Soil bund and the conventional practice. However, AquaCrop did not overestimate biomass for calibrated data set, 0.91 ≤ b ≤ 0.99 with 2.25 ≤ PBIAS ≤ 11.81%. Despite over or underestimation, the PBIAS is within the acceptable limit for agronomic modelling (Ma et al., 2011). At maturity, the measured and simulated final biomasses were highly correlated with low RMSE (0.14 t ha−1) which constituted about 10% of the mean of the measured biomass; NRMSE = 9.33%; EF= 0.98 and d-index = 0.99 with PBIAS = 0.73% for the calibrated data set while for the validated data set, RMSE (0.07 t ha−1) about 2.75% of the means of observed biomass; NRMSE (2.81%); EF (-0.30) and dindex = 0.99 with PBIAS = 0.40% (Fig. 8). Pooled over the seasons for the biomass, y = 0.29 + 0.88x, r2 = 0.95, p ≤ 0.0001. Estimation errors are low and fall within the acceptable 15% limit for agronomic studies and therefore the calibration is rated very good (Hanson et al., 1999). High R2 and d-index in the simulated CC and above ground biomass indicates that CC and biomass sub-models in AquaCrop explain most of the variances in their data. High EF = 0.98 for the calibrated data set in this study indicates that the residual variance was smaller than the measured data variance. However, for the validated data set, mean of measured biomass set is a better predictor than the simulated data set. The deviations of the simulated biomass from 1:1 line are likely due to the weakness of the model in predicting biomass (Fig. 8). AquaCrop overestimated biomass (b > 1.00) in the validated data set except for Tied ridge plus mulch and Mulch plus soil bund but there was no over prediction in the calibrated data set. Results of ‘goodness-of-fit’ for CC and above ground biomass in this study compares well with those reported for soybeans (Khoshravesh et al., 2012; Paredes et al., 2015). Poor performances of the model in simulating biomass in the stated treatments shows that the parameters relating to the crop physiology, phenology and HI need to be calibrated properly using local data and there is a need for proper adjustment of sub-model for predicting biomass in AquaCrop. Therefore, in-spite of the deviations, it could be concluded that AquaCrop performance in simulating biomass of soybeans under rainfed conditions is good when appropriately adjustment of the parameters are made.

validated data set it ranged from -3% for TR to 2% for ML (Table 6). After appropriate calibration of the model, the deviations between the measured and predicted grain yields were far below 15%, the recommended limit for agronomic study (Hanson et al., 1999). Deviation for the calibrated data set ranged from 0% for TR to 3% for BD while for the validated data set it ranged from -3% for TR to 2% for ML. Our results compare well with deviation of about 8.6% for the calibrated grain yields of soybeans using AquaCrop (Paredes et al., 2015). There is a good correlation between the predicted and measured grain yields for the calibrated data set in 2012, R2 = 0.99; low estimation errors, RMSE = 0.04 t ha-1 which about 1.70% of the mean of measured yields; NRMSE = 0.81%; EF = 1.00 and d-index = 1.00 and PBIAS = 0.07% (Fig. 9). Similarly, with the validated data set in 2011, R2 = 0.99; RMSE ≤ 0.02 t ha-1; NRMSE = 0.31%; EF = 1.00; d-index = 1.00 and PBIAS = 0.03%. This means that the model explained 99% of the variability in the grain yields of the crop in the two seasons and that the simulated grain yields were very close to the measured yields. The calibration and validation results could be described as excellent because they fall below the 15% proposed by Hanson et al. (1999) and very good due to high R2, EF and d-index recommended by Qi et al. (2011). Deviation between the measured and predicted grain yields of corn and soybeans using DSSAT ranged between -12 to 27% (Liu et al., 2011). AquaCrop simulated yield sprinkler irrigated soybeans with estimation error less than 23% (Khoshravesh et al., 2012). AquaCrop simulated the grain yield of soybeans with R2 ≥ 0.83 and EF ≥ 0.75 (Paredes et al., 2015). CERES modelled grain yields of wheat with R2 ≤ 0.49 (Chipanshi et al., 1999) while MODIS NDVI explained 53 to 89% of the grain yields of field pea (Mkhabela et al., 2011). CROPGROsoybean model predicted the grain yield of soybeans with a variance of 95% (Jagtap and Jones, 2002). AquaCrop compares well with CROPGRO that simulated water limiting potential and actual grain yields of soybeans with R2 of 0.59 and 0.33 respectively (Bhatia et al., 2008) and 0.88 ≤ R2 ≥ 0.94 for simulated and measured grain yield (Dogan et al., 2007). Overestimation is probably due to under estimation of transpiration, which is very crucial to computation of grain yields in AquaCrop as stated earlier (Table 7). Challenges in estimating the basal crop coefficient is responsible for the under estimation of transpiration in AquaCrop during simulations. Therefore, inadequate partitioning of the components of ET, that is, transpiration and evaporation most likely causes overestimation of the grain yields in AquaCrop (Nyathi et al., 2018). Therefore, deviation of grain yields from 1:1 line could be attributed to underestimation of transpiration because reduction of non-productive evaporation by 50% may not necessarily affect grain yields. The grain yields and biomass clearly show the need for proper calibration of associated parameters in the model. Despite the deviations,

3.1.5. Grain yield The grain yield ranged from 1.56 for NC to 2.96 t ha−1 in 2011 while in 2012 it ranged from 1.64 for NC to 3.25 t ha−1 for MLBD. The differences in the grain yield could be attributed to the differences in environmental conditions in the growing seasons and soil surface management practices. Rainfall in 2012 was greater than that of 2011. Of this seasonal rainfall, 241 mm (31.8%) fell during the reproductive stages in 2012. The overall difference between the measured and predicted grain yields in both seasons was 0.02 t ha−1. Deviation for the calibrated data set ranged from 0% for TR to 3% for BD while for the 1140

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Fig. 6. Measured and simulated soil water storage for rainfed soybeans in the year 2011 using the validated data set.

the pooled data set’s RMSE = 0.03 t ha−1, 1.30% of the mean of measured grain yields; EF = 1.00 and d-index of 1.00 implies that the simulation of the grain yields is within the category of ‘very good simulation result’ as proposed by Brisson et al. (2002) and Ma et al. (2011) because deviation and estimation error are lower than 15%. In the current study, results suggest that grain yields of rainfed soybeans

can be predicted with good reliability using FAO AquaCrop model. 3.1.6. Water productivity The relationship between the predicted and measured WP is shown in Fig. 10. For the calibrated parameters, the model explained 88% of the variance in WP of the crop. The RMSE = 0.08 kg m−3, 13.20% of 1141

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Table 6 Measured and predicted data sets for aerial biomass, crop evapotranspiration, grain yield and water productivity of soybeans under water conservation practices. Year 2011

2012

Treatment label

TR BD ML TRML TRBD MLBD NC TR BD ML TRML TRBD MLBD NC

Biomass (t ha−1) Obs. 1.62 1.24 1.42 1.25 2.11 1.02 1.10 2.16 3.25 3.11 2.24 1.85 3.12 2.12

Sim. 1.69 1.49 1.46 1.41 2.54 1.05 1.22 2.03 2.92 3.09 2.27 1.89 3.09 2.11

Deviation ( ± %)

ETa (mm)

Pd 4 20 3 13 20 3 11 −6 −10 −1 1 2 −1 0

Obs. 351 373 404 338 406 311 308 562 589 533 562 547 562 556

Sim. 430 415 363 359 361 296 419 544 548 545 456 466 452 514

Deviation ( ± %)

Grain yield (t ha−1)

Deviation ( ± %)

Water productivity (kg m−3)

Deviation ( ± %)

Pd 23 11 −10 6 −11 −5 36 −3 −7 2 −19 −15 −20 −8

Obs. 1.68 2.48 2.20 2.12 2.95 2.49 1.56 2.54 3.01 2.21 2.2 1.83 3.25 1.64

Pd −3 0 2 0 0 0 1 0 3 1 1 0 2 2

Obs. 0.47 0.67 0.54 0.62 0.73 0.80 0.51 0.45 0.51 0.42 0.39 0.34 0.58 0.29

Pd −19 −10 15 −5 12 5 −25 4 10 −2 26 15 26 10

Sim. 1.63 2.47 2.24 2.12 2.95 2.49 1.57 2.55 3.09 2.24 2.22 1.83 3.30 1.67

Sim. 0.38 0.60 0.62 0.59 0.82 0.84 0.38 0.47 0.56 0.41 0.49 0.39 0.73 0.32

water-limited environment, water management practice(s) that ensure highest land and water productivity under rainfed conditions are advocated. In this study, the water conservation practices had higher soil water storage, grain yield and water productivity compared with the conventional practice. Generally, successful cultivation of crops requires combining land and other resources. Soybeans growers under rainfed conditions are interested in agronomic and water conservation practices that prevent crop failure due to water stress, increase their income and ensure sustainable land use. Educated farmers and water resources engineers in the region can use the calibration in the current study to optimise land and water productivity of the crop. Key input parameters that will be localised for an environment are the soil, crop variety and meteorological data. Extension agents in the basin can use different extreme weather events, and soils to simulate yields and advice farmers on the most productive approach in optimising productivity of the crop and ensuring climate resilient farming system in the region. Governmental agencies such as river basin and rural development authority and NGOs, consulting engineers, can use the output of the study to plan water allocations for crop production in the area especially now that there is competition for water. Economists can use our calibration and outputs to plan and maximise financial and material resources for production of the crop. In addition, policy makers can use our calibrations to advocate the adoption of water conservation practices as resilient measure and coping strategy for risks related issue and uncertainty in managing water for production of the crops.

Fig. 7. Simulated and measured crop evapotranspiration for rainfed soybeans in the years 2011 and 2012.

the measured averaged WP; NRMSE 7.56%. The EF = 0.95; and dindex = 0.98 shows close agreement between the measured and predicted WP. Using the validated data set, R2 was 0.85; RMSE = 0.07 kg m−3; NRMSE = 9.05%; EF = 0.34 and d-index = 0.97. AquaCrop simulated WP of soybeans with d-index ≥ 0.95 (Khoshravesh et al., 2012). Low but positive EF show that residual variance was smaller than the measured data variance in the cropping seasons and estimation error is lower and within the acceptable limits for the calibrated data set. Deviations in the simulated WP for the calibrated parameters were high (−2 to 26%), and validated data set (−25 to 12%), (Table 6). The deviations testify to the fact that AquaCrop did not simulate WP adequately. Large deviation from 1:1 could be attributed to challenges in simulating ET in the model as stated earlier because reduction of evaporation by 50% in the field management profile where organic mulch simulated lowered ET and affected estimation of WP in the model. The model overestimated WP with b = 1.04 and PBIAS = 1.17% in the pooled data set. In this study, the water conservation practices had higher average ET than the conventional practice. This is attributed to the management of soil surface to eliminate surface runoff and concentrate rain water in the root zone. The average grain yield for Tied ridge and Mulch plus soil bund were 24.2 and 44.3% respectively higher than the average grain yield of conventional practice. The average water productivity for Tied ridge was higher than that of the conventional practice by 14%. Furthermore, the average water productivity for Mulch plus soil bund was 41.9% higher than water productivity for conventional practice. In

4. Conclusion AquaCrop Version 5.0 was calibrated and validated to simulate canopy cover, grain yields, above ground biomass, evapotranspiration, soil water storage and water productivity of rainfed soybeans under soil conservation and conventional practices for two consecutive seasons. The parametization was tested successfully, which resulted in the low deviations and estimation errors in simulating soil canopy cover. There was excellent agreement between the measured and simulated canopy cover which is very crucial to the prediction of soil water, biomass and grain yield in the model. The model overestimated soil water storage at the early and late stages of the crop development and under estimated it below field capacity during reproductions of the crop for water conservation and conventional practices. AquaCrop is not biased in simulating soil water storage because the variability was well captured along the growing seasons. Despite the over and under estimations of soil water storage, estimation error is far below the threshold recommended for agricultural models. AquaCrop showed poor aptitude in estimating evapotranspiration 1142

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Fig. 8. Measured and simulated dry above ground biomass for rainfed soybeans in the year 2012 using the calibrated parameters.

and water productivity for the formation of grain yields and could be attributed to poor partitioning of the components of evapotranspiration. Poor partitioning of the transpiration impacted evapotranspiration and water productivity which showed high deviations from 1:1 line. Therefore, there is a need to adjust the sub-model for estimating transpiration and evaporation in the model in order to ensure high degree of

agreement between the measured and predicted variables. The model needs to be tested under rainfed conditions using other varieties of soybeans and agro-ecological conditions in Nigeria in order to fully ascertain and generalise its performance. Considering the robustness of the model, its simplicity, and performances in the current study, AquaCrop could be judged adequate for the prediction of canopy cover, 1143

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Table 7 Statistical indicators of the performance of AquaCrop in simulating above ground biomass for rainfed soybeans in Ile-Ife, Nigeria. Year

Treatment Label

Regression Coefficient (b)

R2

RMSE (t ha−1)

NRMSE (%)

EF

d-index

PBIAS (%)

2011

TR BD ML TRML TRBD MLBD NC TR BD ML TRML TRBD MLBD NC

1.10 1.04 1.00 0.93 1.01 0.96 1.06 0.90 0.91 0.93 0.93 0.98 0.95 0.99

1.00 0.99 1.00 0.99 0.99 0.99 0.99 0.99 0.98 0.99 0.99 0.99 0.99 0.99

0.10 0.10 0.10 0.10 0.20 0.10 0.20 0.18 0.25 0.20 0.16 0.13 0.19 0.10

8.0 10.7 6.6 7.9 10.6 10.1 10.4 16.8 16.5 13.0 15.5 13.4 11.5 8.2

0.99 0.97 0.99 0.98 0.97 0.97 0.97 0.95 0.93 0.97 0.96 0.97 0.97 0.98

1.00 0.99 1.00 0.99 0.99 0.99 0.99 0.99 0.98 0.99 0.99 0.99 0.99 0.99

−9.34 −4.97 0.72 6.60 0.44 6.07 −5.20 11.81 11.17 10.40 13.25 4.91 5.62 2.25

2012

Fig. 9. Measured and simulated grain yields at maturity for rainfed soybeans in the years 2011 and 2012 in Ile-Ife, Nigeria using the validated and calibrated parameters respectively.

Fig. 10. Simulated water productivity for rainfed soybeans in the seasons using the calibrated and validated parameters respectively.

above ground biomass and grain yield under water conservation and conventional agronomic practices in the cultivation of soybeans.

during the fieldwork. We appreciate the International Institute For Tropical Agriculture, Ibadan, Nigeria for providing advisory services on the cultivar of soybeans used for the study. We appreciate anonymous reviewers for their constructive criticisms.

Acknowledgement

References

The first author appreciates the Dutch government under Netherlands Fellowship Programme for financing this study. We would like to thank Prof. O. O. Jegede of the Department of Physics, Obafemi Awolowo University, Nigeria for sharing weather data with us during the study. We thank the technologist who rendered technical assistance

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