Modelling and measurement of water productivity and total evaporation in a dryland soybean crop

Agricultural and Forest Meteorology 266–267 (2019) 65–72

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Modelling and measurement of water productivity and total evaporation in a dryland soybean crop

T

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N.C. Mbangiwaa,b, , M.J. Savagea, T. Mabhaudhic a

Agrometeorology Discipline, Soil-Plant-Atmosphere Continuum Research Unit, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, South Africa b Department of Physics, University of Botswana, Private Bag UB 00704, Gaborone, Botswana c Crop Science Discipline, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, South Africa

A R T I C LE I N FO

A B S T R A C T

Keywords: AquaCrop Bowen ratio Crop coefficient Eddy covariance Evapotranspiration

Simpler crop models simulating evaporation are needed to provide information to farmers, policy makers and decision makers on how to maximise crop yield responses to water. This is becoming important as the frequency and severity of droughts in South Africa is increasing. In this regard, prediction of yield, determination of water productivity and total evaporation (ET) are increasingly becoming essential in water resource management. The overall objective of the study was to compare the FAO AquaCrop daily model output of ET to the residual ET for non-stressed dryland soybean in a sub-humid climate. Energy balance residual ET estimates using an eddy covariance (EC) system and modelled ET using AquaCrop obtained from Glycine max (L.) Merrill grown in the midlands of KwaZulu-Natal, South Africa during the 2012/13 growing season are compared. The modelled and observed yield showed good agreement, while the residual ET was 21.6% less than the modelled. The energy balance closure computed using the daily sums of sensible heat and latent energy fluxes against daily available energy flux for unstable atmospheric conditions was 0.77. A closure of 0.99 was achieved when the EC latent energy flux was replaced with residual latent energy flux. A good fit between the modelled and observed percentage green canopy cover was observed (slope = 0.86, intercept = 15.46%, root mean square error = 10.50% and R2 = 0.83). Season-long daily residual ET values were consistently low for most of the growth stages compared to the modelled, except for the maturity stage. However, the residual ET comparisons with the AquaCrop model improved after gap-filling was applied to discarded data and for when the EC system failed.

1. Introduction Soybean is an important economic crop in South Africa, regionally and globally, used for human and animal consumption as well as being an industrial raw material (Dlamini et al., 2014; Siebers et al., 2015; Khojely et al., 2018). South Africa is one of the largest soybean producers in sub-Saharan Africa with an average yield reported at 2.23 t ha−1 for the period 2012–2016 (Khojely et al., 2018). Despite being grown in almost all provinces of South Africa, soybean production efforts are being affected by recurring droughts. The 2015/16 drought resulted in a reduction of soybean yield by 32%, much of this due to a reduction by 27% of the area planted (DAFF, 2016). Similarly, soybean production is significantly affected by drought and increased temperatures (Lal et al., 1999; Siebers et al., 2015; Jin et al., 2018). Furthermore, climate models have indicated that climate variability alone will decrease global crop production by 9% by 2050 (Rost et al., 2009).

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The challenge faced by agrometeorologists, agronomists and hydrologists globally is therefore an increased need for climate, soil and plant information that accurately quantifies water use of various crops (Perarnaud et al., 2005; Balaghi et al., 2010; Fisher et al., 2017; Meyer, 2018). In this regard, there is a need for testing various remote sensing products, crop models and modern climate monitoring equipment for measuring evapotranspiration. The processes of evaporation and transpiration are crucial components of the hydrological cycle. Evaporation refers to the change of phase of liquid water to water vapour (Ackerman and Knox, 2007). Conversely, transpiration involves the movement of liquid water in plant tissue and subsequent loss via the stomata to the atmosphere as water vapour (Allen et al., 1998). These processes play a crucial role in determining energy and mass exchanges in the soil-plant-atmosphere continuum. The combined water loss by evaporation from the soil, water and transpiration from plants is commonly referred to as total

Corresponding author at: Department of Physics, University of Botswana, Private Bag UB 00704, Gaborone, Botswana. E-mail address: [email protected] (N.C. Mbangiwa).

https://doi.org/10.1016/j.agrformet.2018.12.005 Received 5 September 2017; Received in revised form 1 December 2018; Accepted 6 December 2018 0168-1923/ © 2018 Elsevier B.V. All rights reserved.

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evaporation or evapotranspiration (ET, mm) (Zhang et al., 2007; Foken et al., 2012). Knowledge of ET is important for water resources management (Djaman and Irmak, 2013), especially in water-scarce areas such as South Africa. The total water withdrawal in South Africa was estimated at 15,500 million m3 a−1 for the period 2013–2017, with agricultural withdrawals accounting for 62.5% (FAO, 2018). Accurate estimation of ET is important for the determination of water productivity (WP, kg m3 ) – a measure of the marketable crop yield (kg ha−1) per unit water used. In this study, water used is taken to be ET (m3 ha−1) (Zwart and Bastiaanssen, 2004; Liu et al., 2007; Nhamo et al., 2016). An understanding of WP is crucial to efforts to increase crop productivity without increasing water withdrawals. In this regard, it becomes important to have reliable values of ET. Several micrometeorological techniques such as Bowen ratio energy balance (BREB), eddy covariance (EC), lysimetry, scintillometry, and surface renewal (SR) have been used to estimate ET over a range of surfaces (Allen et al., 1991; Drexler et al., 2004; Odhiambo and Savage, 2009; Savage, 2009; Mengistu and Savage, 2010; Moyo, 2011). While many micrometeorological techniques exist, these techniques present unique merits and shortcomings. The main challenges are related to cost and lack of technical expertise in micrometeorology and equipment handling (Foken et al., 2012). To overcome some of these challenges, crop simulation models may be used for estimating various water accounting outputs and to analyse crop responses to changing climatic factors. Furthermore, crop models may be used to investigate the effect of various management practices and subsequently decide on the best management practise recommendations. Many crop models have been well tested across different regions worldwide (Porter et al., 1993; Abraha and Savage, 2006; Todorovic et al., 2009; Palosuo et al., 2011; Rötter et al., 2012). Among the existing crop models, the FAO AquaCrop is a water-driven model that is much simpler and robust with fewer input requirements (Steduto et al., 2009). Several studies have shown that AquaCrop is comparable to other crop models (Todorovic et al., 2009; Saab et al., 2015). Longterm field measurements of mass and energy exchange of economic crops like soybeans are sparse locally, regionally and globally. Thus, this study will provide crucial insights on current micrometeorological methods and use of crop models (e.g. AquaCrop). Therefore, the aim of this study was to evaluate the yield, ET and WP of soybean (Glycine max (L.) Merrill), grown under rainfed conditions in a sub-humid climate using FAO AquaCrop and the EC method.

Table 1 Soil particle size analysis and texture class at the experimental site. Soil name

Huttona

Profile depth (m)

Texture class

Clay % (< 0.002mm)

Fine silt % (0.0020.02mm)

Coarse silt and sand % (0.02-2mm)

0-0.1 0.4-0.5 0.9-1.0

Clay Clay Clay

54 62 72

18 13 11

28 25 17

a Hutton form (Hu) per soil classification taxonomic system for South Africa. (Soil Classification Working Group, 1991).

layers. Soil pH ranged from 4.60 at 0-0.1 m depth to 5.18 at 0.9–1.0 m depth and soil organic carbon ranged from 1.00 to 3.70%, respectively. The fertilizer application rates were 10 kg N ha−1, 30 kg P ha−1 and 40 kg K ha−1. Maize crop residues (1.09 kg m-2) from the previous season (2011/12) were visible on the soybean field. Three time-domain reflectometry (TDR) probes were inserted horizontally into the soil at 0.1, 0.5 and 1.0 m depths for monitoring the profile soil water content. The soybean cultivar (PAN 666R) seeds were planted (using a PDM PG MAXIMA Model 1200 planter) and harvested (using a John Deere Model 9670 STS combine harvester) on 15th October 2012 and 27th March 2013, respectively. The seeds were sown at 0.76-m row spacing and 0.035 m within rows, translating to approximately 380,000 plant ha−1. PAN 666R is a medium maturing cultivar (∼138 days) attaining an average height of 1 m (PANNAR, 2012). Blignaut and Taute (2010) provide a detailed analysis of soybean production regions in South Africa. Herbicides and pesticides were applied using sprayers mounted on tractors but areas closest to the EC tower were hand sprayed. Glyphosate (Roundup Ready), Chlorimuron-ethyl (Classic) herbicides and Lambda-cyhalothrin insecticide were applied two weeks after emergence and 21 days later. Phytophthora-Arten fungicide and Lambda were applied 60 days after emergence and then 30 days later. A plant canopy analyser (Table 3) was used to measure leaf area index (LAI) every two weeks from germination to harvesting. The green canopy cover (CC) was determined using the diffuse non-interceptance (DIFN), which is one of the outputs of the LAI-2200 canopy analyzer (Mabhaudhi et al., 2014), using:

CC = 1 − DIFN Average plant height (m) was also measured every two weeks using a standard 5-m tape measure (from six randomly selected plants). The periodic visual observation of leaf colour was used to determine the onset of senescence. Seed yield was obtained at the end of the season using a combine harvester and as an average of three replicates of 1 m2 quadrat manual harvesting.

2. Materials and methods 2.1. Site description and agronomic practices Micrometeorological and meteorological measurements from Baynesfield Estate (near Richmond), KwaZulu-Natal, South Africa (altitude 840 m; 30.358 °E; 29.765 °S), over a soybean field for one season (2012/13) were collected. The experimental site chosen had a fetch greater than 100 m in all directions. The climate of the area is classified as sub-humid and characterized by dry cool winters and warm rainy summers with a mean annual precipitation of 844 mm (Mengistu et al., 2014) and a mean annual temperature of 17.2 °C (Ngwenya, 2012). The cropping system at Baynesfield Estate is based on conservation agriculture and is characterised by a maize-soybean rotation. Fallow land is experienced from the last day of harvesting to the beginning of each planting season. The dominant soil type at Baynesfield Estate was classified as Hutton form (Hu) (per soil classification taxonomic system for South Africa) (Soil Classification Working Group, 1991) or Haplic Ferralsol (World Reference Base for Soil Resources, WRB) (Fey, 2010). Bulked soil samples at 0-0.1, 0.4-0.5 and 0.9–1.0 m layers were taken from the experimental site prior to planting and analysed for soil texture (Table 1) and nutrients (Table 2). There was no sampling between the

2.2. AquaCrop model The FAO’s AquaCrop model (v.4.0) is freely available software that simulates, among many other things, seed yield and ET (Vanuytrecht et al., 2014). It is a water-driven daily crop simulation model that can simulate reasonable yields of several crops as a function of water productivity under rainfed, deficit or full irrigation conditions (Raes et al., 2009; Steduto et al., 2009). The model utilizes the cumulative actual transpiration (Tr, m3) during the growing season and normalized water productivity (WP, kg m−3) for simulating the total biomass (B, kg) as:

B = WP ×

∑ Tr

The harvestable yield (Y) is determined using B and harvest index (HI) from:

Y = B × HI The AquaCrop model is relatively simple to use (Vanuytrecht et al., 2014), with a few input parameters and hence has greater appeal than 66

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Table 2 Chemical properties of the soil at the experimental site. Profile depth (m)

Bulk density (kg m−3)

P K Ca _______________(kg m−3)*10−3___________

Mg

Zn

Mn

Cu

OCa ___(%)__

N

pH (KCl)

0-0.1 0.4-0.5 0.9-1.0

990 1030 980

50.0 1.0 4.0

244.0 178.0 436.0

10.2 0.4 1.3

20.0 3.0 4.0

9.0 3.2 2.6

3.7 2.4 1.0

0.3 0.2 0.1

4.6 4.7 5.2

a

487.0 29.0 105.0

1351.0 1020.0 534.0

Organic carbon (mid-infrared estimate).

other crop models (Farahani et al., 2009). AquaCrop requires climate (*.CLI), soil (*.SOL) and crop (*.CRO) input files to run. The climate file comprised air temperature (*.TMP), rainfall (*.PLU), reference evapotranspiration (*.ETo), and a default file for CO2 concentration (*.CO2). All climate input parameters used in the model were obtained from the automatic weather station (AWS) near the experimental field except for the default CO2 concentration file based on measurements from Mauna Loa (altitude 3394.21 m; 19.536 °N; 155.576 °W). The reference CO2 value used was 391.57 ppm for year 2011. The daily meteorological data (air temperature, relative humidity, wind speed and solar irradiance) from the AWS were used to compute daily ETo using the Penman-Monteith method. A list of crop parameters set in the model is shown in Table 4. The sensitivity of AquaCrop to weather input data was conducted by using the daily, 10-daily and monthly AWS data (reference evapotranspiration, air temperature and rainfall) obtained from the experimental field. For each of the model simulations using each of the weather inputs, the soil, crop and default atmospheric CO2 concentration files were not altered.

Table 4 List of input parameters for the AquaCrop model for soybean in the sub-humid environment of the Midlands of KwaZulu-Natal, South Africa. Parameter Base temperature (Tbase) Cut-off temperature (Tupper) Seedling leaf area Initial canopy cover (CCo) Canopy growth coefficient (CGC) Canopy decline coefficient (CDC) Maximum canopy cover (CCx) Normalized water productivity (WP) Reference harvest index (HIo) Positive effect of HI due to limited growth in vegetative period Plant density Time to 90 % emergence Time to maximum canopy cover Time to flowering Duration of flowering Time to senescence Time to maturity Minimum rooting depth Zrmin Maximum rooting depth Zrmax

2.3. Eddy covariance measurements The experimental site included an instrumentation mast, deployed to maximise fetch, which was installed in the middle of the field (42.46 ha) and was fully instrumented with an EC system. Disturbance to the crops and area around the mast was minimized to maintain crop uniformity across the field. Fluxes of CO2, latent energy (LE) and sensible heat (H) and momentum using the EC method were measured continuously throughout the growing season. The EC equipment was mounted 2.0 m above the plant canopy and periodically adjusted to maintain the same height throughout the season. The EC system consisted of a three-dimensional sonic anemometer coupled to an openpath infrared gas analyser (Table 3). Data were recorded using a

Value

Unit

5 30 5 1.64 10.5 0.19 98 15.0 45 10

o

380,000 112 951 934 475 1476 1881 0.30 2.00

C C cm2 % % day−1 % day−1 % g m−2 % %

o

plants ha−1 GDD GDD GDD GDD GDD GDD m m

Campbell CR3000 datalogger. A measurement frequency of 10 Hz, averaged every 30 min, was used for the datalogger. All data were stored on 1- or 2-Gbyte compact flash cards. Two pairs of soil heat flux (G) plates, averaging soil temperature thermocouples and soil water content sensors (Table 3) were used. Soil heat flux plates and soil thermocouples were each deployed along the rows and between the rows to give a representation of the soil heat flux of the area. Soil heat flux plate data were corrected for heat storage using the soil temperature (°C) and soil water content (m3 m−3). The sonic anemometer sensible heat flux density H was obtained

Table 3 Experimental site measurements and EC tower system details. Site details

Instrument details

EC Tower

3D sonic anemometer (CSAT3Aa) collocated with open path CO2/H2O gas analyser (EC150a) and air temperature probe (EC150a) in six-plate Gill radiation shield, sensor heights maintained at 2 m above plant canopy; net radiometer (NR LITEc) at 3 m above ground; solar irradiance (total incoming and reflected) (CM3c) at 3 m above ground; HMP45AC - RH and air temperatured, height maintained at 2 m above plant canopy; profile soil water content sensors (CS616a), at 0.1, 0.5 and 1.0 m depth; one CS616 soil heat flux plates (HFP015C-15f) and type-E thermocouple soil averagera at 0.08 m; IRTe at 3 m above ground; plant canopy analyser (LAI-22002), was used to obtain soybean LAI CS500 RH and air temperatured, solar irradiance (LI200Xb) and wind speed and directiong model 03001, at 2 m; rain gauge (TE252MMh) - rim at 1.5 m CR200a (all measurements were taken every 10 s and averaged/totalled every 60 min), CR3000a (measurements at high frequency (10 Hz) and slow measurements scanned every 10 s and averaged every 30 min). Both high frequency and 30-min data were collected to 1- or 2-G byte cards except for the AWS where a cell phone modem was used to do a scheduled data collection. The AWS was powered from a 12 V 14 A h battery connected to a solar panel; the soybean EC system was powered from two 103 A h batteries connected in parallel and charged from a 50 W solar panel

AWS Site dataloggers

Power

a b c d e f g h

Campbell Scientific, Inc., Logan, Utah, USA. LICOR, Lincoln, Nebraska, USA. Kipp and Zonen B.V., Delft, The Netherlands. Vaisala Oyj, Helsinki, Finland. Apogee IRT model SI-111 (half angle of 22°): Apogee Instruments, Inc., Logan, Utah, USA. HuksefluxUSA, Inc., Manorville, New York, USA. RM Young Company, Traverse City, Michigan, USA. Texas Electronics, Inc., Dallas, Texas, USA. 67

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from the covariance between measured vertical wind speed and sonic temperature as follows:

H = ρa Cp w′Ts′ where ρa is the density of air, Cp the specific heat capacity of air, and w′ and Ts′ the vertical wind speed and sonic temperature fluctuations, respectively. The primes denote instantaneous fluctuations about the mean. The theoretical basis for the EC method is described by Foken et al. (2012). 2.3.1. Data processing and gap-filling The EC raw high frequency data were processed post data collection using the EddyPro (v6.2.0) (www.licor.com/eddypro) software. The processing included corrections for water vapour density fluctuations, tilt corrections, block averaging, time lag compensation, de-spiking, amplitude and spectral corrections. Gap-filling was applied to the EddyPro 30-min H and LE output data using a marginal distribution sampling (MDS) gap-filling algorithm (Reichstein et al., 2005). 2.3.2. Energy balance closure The energy balance closure was forced using the “residual-LE closure” and “Bowen ratio closure” methods following recommendations by Twine et al. (2000). In the residual-LE closure method, LE is calculated as a residual from the shortened energy balance equation (Rn = H + LE + G) from measurements of sensible heat flux H, net irradiance Rn and soil heat flux G. For the Bowen ratio closure method, the turbulent fluxes of H and LE are independently adjusted to balance the shortened energy balance equation using the Bowen ratio β measured by the EC system.

Fig. 1. (a) Measured LAI and plant height (plant_h) at the experimental site and (b) the observed crop cover (CCo) and simulated crop cover (CCs) from 32 days after planting (DAP) (15th November 2012) to maturity covering crop growth stages 2 to 4 for the 2012/13 growing season. CC refers to the green canopy cover (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

comparison between AquaCrop modelled ET and eddy covariance ET residual and energy balance closure methods will be discussed followed by the discussion on modelled and observed single crop coefficient (Kc) values. Furthermore, the soybean modelled and observed yield and water productivity will be discussed. Lastly, the response of soybean to climate variability will be discussed. The residual ET reported in this study refers to the ET residual obtained using the residual-LE closure method recommended by Twine et al. (2000).

2.4. Monitoring the microclimate An AWS (altitude 846 m; 30.339 °E; 29.764 °S) (Table 3) was used to collect standard meteorological data. The AWS included a pyranometer sensor placed at 2.0 m above ground, air temperature and relative humidity sensor placed at 1.5 m height, a wind speed and direction sensor also placed at 2.0 m and a tipping bucket rain gauge. A CR200 datalogger and a scan interval of 10 s was used to output data every 15 min. The summary of the observed meteorological variables during the study is shown in Table 5.

3.1. Plant canopy One of the important parameters used to define canopy structure is LAI (Chen et al., 1997). This is the main variable used to model various processes in the ecosystem including evapotranspiration (Bréda, 2003; Weiss et al., 2004). AquaCrop simulates green canopy cover (CC) rather than LAI. The use of CC as opposed to LAI is meant to simplify the model since CC can be estimated where there is no direct measurement of LAI. Direct measurements of LAI were started 32 days after planting (DAP) and ranged from 0.03 to 6.49 m2 m−2 (Fig. 1(a)). The observed LAI increased as the soybean canopy size increased, peaked, and then decreased at senescence. The plant height followed the same trend. Growth stopped at the maturity stage, with the crop attaining an average height of 1.08 m (Fig. 1(a)). Results of model testing showed a good fit between the simulated and observed CC under rainfed conditions (slope = 0.86, intercept = 15.46%, RMSE = 10.50% and R2 = 0.83) (Fig. 1(b)).

3. Results and discussion Based on the measurements, soybean leaf area index and derived green canopy cover followed by the soil water and soil temperature dynamics will be discussed in the following sub-sections. Then the Table 5 Summary of monthly averages and totals for selected meteorological variables during the 2012/13 soybean growing season. Season (2012/13) Month

Tn b Tx a ––––––(oC)–––––

Uc (m s−1)

Is d (MJ m−2)

Rainfall ETo e ––––––(mm)–––––

October November December January February March April

21.5 22.5 26.6 26.7 27.4 26.7 25.4

1.46 1.35 1.18 1.07 1.02 1.04 1.16

11.6 14.6 18.1 16.4 17.2 14.2 13.4

119.9 94.5 74.4 122.2 80.3 53.8 99.8

a b c d e

12.2 12.7 15.6 16.0 15.3 14.6 10.6

71.5 85.5 114.4 106.3 99.6 91.0 86.5

3.2. Soil water and soil temperature The measured total rainfall from October 2012 to April 2013 was 644.9 mm while the average soil temperature at 0.08 m for the growing season ranged from 10.0 to 41.7 °C. The volumetric water content (θ) (m3 m−3) was observed at three soil depths (0.1, 0.5 and 1.0 m). The soil water content closer to the surface (0.1 m) was variable and showed sharp peaks corresponding to rainfall events (Fig. 2). The observed variability could be caused by the wetting and drying cycles since the 0

Maximum air temperature. Minimum air temperature. Average wind speed. Monthly daily total solar irradiance. Monthly total short-grass reference evapotranspiration. 68

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Fig. 2. Measured daily volumetric soil water content (θ) at 0.1, 0.5 and 1.0 m soil depths and rainfall from DOY 314 (2012) to 101 (2013) (DAP 28 to 130), respectively.

to 0.1 m zone is closer to the surface from which soil evaporation occurs. The maximum and minimum θ observed from DOY 314 until harvesting was 0.40 and 0.24 m3 m−3 at 0.1 m, 0.45 and 0.34 m3 m−3 at 0.5 m and 0.53 and 0.4 m3 m−3 at the 1.0-m depth, respectively. In a similar study, the water content data reported by Sauer et al. (2007) at the 0.06-m depth for a mesic Typic Hapludoll, Aquic Hapludolls and Typic Endoaquolls soils showed a similar pattern to that reported in this study. The 0.5-m depth θ showed less daily variability and the response to rain was slightly delayed as the water slowly infiltrated the soil. At the deeper soil depth (1.0 m), there was no diurnal variability in θ. 3.3. Sensitivity analysis The sensitivity analysis of the model to weather input data was assessed using the daily, 10-daily and monthly AWS data for the experimental field. The soil, crop and default atmospheric CO2 concentration files were not altered. The use of the 10-daily and monthly weather input data resulted in 26.8% and 77.4% decrease in soybean dry seed yield. The total evaporation was reduced by 17% and 65% for the 10-daily and monthly input data, respectively. A similar pattern was observed for the water productivity which decreased by 15.6% and 34.9%, respectively. This suggests that the model is sensitive to the time scale of the weather input data. For example, the stress due to the stomatal closure was minimal (2%) for the daily data input while it increased to 18 and 41% for the 10-daily and monthly weather inputs, respectively. Regardless of the time aggregation of the input weather data, the model outputs daily estimates for all parameters. This means that use of time scales other than daily will yield less reliable results (Raes et al., 2017). However, further sensitivity analysis of AquaCrop to other input and tuning parameters need to be conducted for more insights.

Fig. 3. (a) Regression analysis of the daily available energy flux (Rn – G) and sum of turbulent energy fluxes (H + LE) from the EC system during the soybean growing season (2012/13). (b) Regression analysis of Rn – G and H + LEresidual (residual-LE). The dashed line is the 1 to 1 line through the origin, the solid line is the linear regression line.

observed soybean energy imbalance (0.77) was comparable with other studies on soybean using similar instrumentation. For example, Hernandez-Ramirez et al. (2010) reported energy imbalance of 0.80 for soybean grown near Ames, Iowa. The observed discrepancy could be caused by the differences in topography (McGloin et al., 2018) and the soybean cultivar used. Most agronomic and management activities were similar between the two study sites. The energy imbalance was also assessed using the residual-LE (Fig. 3 (b). This approach yielded very good results (slope = 0.99, intercept = 0.89 MJ m−2, RMSE = 0.83 MJ m−2 and R2 = 0.99). The results indicated that the energy imbalance after forced closure was approximately 1%. As a result, the use of residual ET (ETresidual) may be assumed to be a better estimate of ET with reduced errors.

3.4. Soybean energy balance closure The energy balance closure is an important measure applied to test if the conservation of energy is satisfied and this is quite important in assessing the quality of the measured surface energy fluxes from EC systems (Twine et al., 2000; Wilson et al., 2002; Shao et al., 2014). An assessment of the energy balance closure during the soybean growth cycle was performed by plotting the sum of daily turbulent energy fluxes (H and LE) against the available energy flux (Rn - G) for unstable atmospheric conditions for the entire soybean growing season (2012/ 13) (Fig. 3 (a)). A good agreement was observed between the available energy flux and the sum of the turbulent fluxes (slope = 0.77, intercept = 2.66 MJ m−2, RMSE = 1.23 MJ m−2 and R2 = 0.91). The 69

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evaporation. This calls for the promotion of soil water conservation methods that can minimise unproductive losses, especially during the early stages of crop development when green canopy cover is still low and much of the ground is bare and exposed. The development stages described in this section are based on the model output of water balance components and detailed stage of development descriptions are given by Fehr et al. (1971). Two methods (residual-LE and Bowen ratio closure) recommended by Twine et al. (2000) were employed to determine ET. The two methods produced similar ET (mm) totals for the season (2012/13), which was 88.4% in agreement. For example, for the residual-LE method, a total ET of 347.5 mm was observed while the Bowen ratio closure method produced a total ET of 335.9 mm for the season. The comparison of the ET from both methods also showed comparable statistical results when compared to AquaCrop model total ET for 130 days (slope = 0.83, intercept = 1.13 mm, R2 = 0.64, RMSE = 1.03 mm and slope = 0.86, intercept = 1.13 mm, R2 = 0.64, RMSE = 1.12 mm) for residual-LE and Bowen ratio closure method, respectively. Average single crop coefficients (Kc) were computed by dividing the modelled and residual ET by the reference evapotranspiration (ETo) (Kc = ET/ETo) for different soybean growth stages on selected rainless days. The Kc was computed using daily totals of ET and ETo. The number of days per growing stage used for Kc calculations is the AquaCrop days after planting (DAP) to allow for comparisons with the observed and simulated Kc. The observed EC Kc values for stages 1–4 were 0.19, 0.59, 0.73 and 1.12, respectively. The simulated Kc values were 0.45, 1.03, 0.87 and 1.01 for stages 1–4, respectively. Both the EC observed and the AquaCrop simulated Kc values followed the same trend as that for FAO56 Kc (Allen et al., 1998) (Fig. 5) but remained high at maturity. The Kc values reported in this study were similar to those found by Suyker and Verma (2009) (0.86 and 0.47) for their 2002 study for dryland soybean. The different Kc values per crop development stage were expected as the growing characteristics of the crop changed with time. An all-season regression analysis of simulated versus observed Kc showed good agreement (R2 = 0.84, RMSE = 0.62). The regression analysis of simulated versus observed Kc per growth stage showed that the Kc relationship improved as the crop matured. The pattern of Kc values increasing with crop growth, peaking at end stage, is similar to other studies (Singh et al., 2014). This Kc pattern is mainly attributed to changes in crop water requirements in relation to increasing leaf area as the canopy develops.

Fig. 4. Simulated (ETs) and EC (ETresidual) evapotranspiration for the 2012/13 growing season. A moving average of order 3 was used. The numbered blocks (1–4) represent the four soybean development stages: (1) emergence (9 days); (2) vegetative (62 days); (3) flowering (29 days) and (4) yield formation and ripening (30 days).

3.5. AquaCrop model ET compared to residual ET The EC measurements reported in this study were during all growth stages of the soybean crop (Fig. 4). The measurements used to compute ET are for unstable atmospheric conditions between 06h00 and 18h00. The results showed that modelled ET was greater (21.6%) than the ETresidual throughout the season (slope = 0.83, intercept = 1.13 mm, R2 = 0.64, RMSE = 1.03 mm). Under-estimation of the ETresidual values is mostly due to data exclusion by EddyPro quality assurance processing. Occasional system failures and rainfall events reduced the ET further. The ETresidual was consistently under-estimated compared to the modelled ET for most of the soybean growth stages (1–3). It is well documented that plant residues suppress soil evaporation (Dao, 1993; Cook et al., 2006; Klocke et al., 2009; Turmel et al., 2015; Vanhie et al., 2015). However, the AquaCrop model did not account for plant residues. Part of the flowering stage and most of the ripening stage showed a more consistent pattern than the emergence and vegetative stages (Fig. 4). The regression analysis for the various development stages showed that stages 1, 2 and 4 (emergence, vegetative and ripening, respectively) had an improved relationship between simulated ET and residual ET (slope = 1.39, intercept = 0.19 mm, R2 = 0.81, RMSE = 0.66 mm, slope = 0.69, intercept = 0.70 mm, R2 = 0.69, RMSE = 2.91 mm, slope = 0.84, intercept = 0.15 mm, R2 = 0.97, RMSE = 2.78 mm), respectively. The flowering stage (3) showed a poor relationship between simulated and residual ET (slope = 0.99, intercept = 0.70 mm, R2 = 0.82, RMSE = 3.15 mm) compared to others. Although the ETresidual followed the same trends as that for AquaCrop, the under-estimation was observed even after gap-filling was applied. The transition between the vegetative and flowering stages showed the highest discrepancy between modelled and residual ET. This could be due to high rainfall observed between stages 2 and 3. The core function of the sonic anemometers depends on the signal acquisition of the transducers hence rain events cause signal loss and subsequently loss of the turbulent fluxes (H and LE). Poor quality of surface energy fluxes has been attributed to rainfall events elsewhere (Timouk et al., 2009). It should be noted that the green canopy cover for stage 1 was almost 0%, making soil evaporation (E) the major contributor to ET. AquaCrop’s ability to partition ET into E and transpiration (T) using a simple canopy level equation allows for effective comparison of productive (transpiration) against unproductive (soil evaporation) water losses. This can then be used to better manage water at a field level and improvements in water productivity. This is of importance for rainfed cropping systems where significant amounts of water are lost by soil

3.6. Yield and water use The soybean seed yield was obtained using both the combine harvester and manual harvesting. The observed combine harvester seed yield was 3.52 t ha−1 while the manually harvested seed yield (kg m-2) extrapolated to the whole field was 5.28 t ha−1 assuming no wastage due to pod shattering. From the field observations, it was evident that the combine harvester was inefficient in capturing the entire seed yield, as 38% of seed yield was unaccounted for. Thus, only the manually harvested seed yield was used for computing other parameters. Field observations showed that some losses occurred before harvesting commenced probably due to pod shattering as the crops were harvested late and were very dry. It has been shown that the combine harvester header causes most soybean field losses (80%) (Nave and Wax, 1971). The model output of seed yield was 4.79 t ha−1, which was 90.7% of the observed (manually harvested) and deemed satisfactory. Water productivity was determined using the simulated and total residual ET (WPs and WPEC), respectively. The WPs and WPEC values were 1.14 and 1.52 kg m−3, respectively. Similar observed WP at 84 DAP (1.43 kg m−3) have been reported elsewhere for intraspecific soybean for one of the seasons and ET was determined using the Ritchie model (Geddes et al., 1979). Previous studies in the Mediterranean region have indicated low soybean WP (for example, in Italy a range of 70

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Fig. 5. Distribution of simulated (Kc_s) and observed (Kc_ob) soybean single crop coefficients per growing stage for selected days. Kc_FAO is the FAO reported Kc values (Allen et al., 1998).

0.47 to 0.77 kg m−3 was observed (Katerji et al., 2008). It should be noted that the study by Katerji et al. (2008) used lysimeters and soil water balance equation for determining ET. For soybean grown under vertisols of India, a three-year average WP of 2.19 kg m−3 was observed (Hati et al., 2006). This study also used the soil water balance method to determine ET. It was observed that WP was influenced by several factors (plant cultivar, weed management, fertility, water regime and climate) affecting the observed variability across regions. Katerji et al. (2008) provide a more detailed discussion on sources of variability.

Acknowledgements The Water Research Commission (WRC) of South Africa is acknowledged for funding through WRC Project No. K5/2066 ‘The validation of the variables (evaporation and soil moisture) in hydrometeorological models’ (WRC, 2014). The University of KwaZulu-Natal is also acknowledged for funding and use of research equipment. We thank the staff of Baynesfield Estate for allowing us to conduct longterm experiments at their facilities. Field assistance by Agrometeorology postgraduates (Ms. S.I. Luthuli, Mr. J.M. Pasi and C. Malapana) is greatly acknowledged. The Agricultural Research Council, Institute for Soil, Climate and Water, Pretoria (ARC-ISCW) provided the AWS data.

4. Conclusions Results comparing ET obtained from AquaCrop and ET using the EC method showed fair agreement (slope = 0.83, intercept = 1.13 mm, R2 = 0.64 and RMSE = 1.03 mm) for the entire 2012/13 growing season. Closer analysis of the comparison between simulated and residual ET for different soybean development stages also exhibited fair agreement (slope ranged from 0.69 to 0.99, intercept ranged between 0.15 to 0.70 mm, R2 ranged between 0.69 and 0.97 and the RMSE ranged between 0.66 and 3.15 mm) for all growth stages. However, a large discrepancy in residual ET was observed during the transition from vegetative to flowering stage where simulated ET remained consistently higher than residual probably due to high rainfall received during that period. The rainfall events caused signal loss between the sonic anemometer transducers resulting in no output for the turbulent energy fluxes (H and LE). Measuring ET and subsequently determining other water accounting parameters such as WP remains a challenge and is costly for routine use. The analysis of residual-LE and Bowen ratio closure methods indicated that the two methods are comparable with the residual-LE closure method performing slightly better compared to the Bowen ratio method. The observed WP was comparable to the simulated WP. The FAO AquaCrop model simulated ET was consistently greater compared to residual ET for most of the growing season except for the ripening stage when the agreement improved. The sensitivity analysis of the model to weather input data indicated that AquaCrop is sensitive to the time scale and results in less accurate outputs for 10daily and monthly inputs. The observed and simulated Kc values for the soybean cultivar used in this study compared well with the mid-season value reported in the FAO-56 table of crop coefficients except for the end stage where the FAO-56 coefficient was the lowest. The model output of seed yield was also in agreement with that observed. However, more field testing with other soybean cultivars is needed to validate the model under different soils and climates. The effect of conservation agriculture practices on crop growth, development, yield and ET require further investigation. Given the challenges encountered, crop simulation models such as AquaCrop remain attractive.

References Abraha, M.G., Savage, M.J., 2006. Potential impacts of climate change on the grain yield of maize for the midlands of KwaZulu-Natal, South Africa. Agric. Ecosyst. Environ. 115, 150–160. Ackerman, S.A., Knox, J.A., 2007. Meteorology: Understanding the Atmosphere, 2nd ed. Thomson Higher Education, Belmont, USA, pp. pp. 467. Allen, R.G., Howell, T.A., Pruitt, W.O., Walter, I.A., Jensen, M.E., 1991. Lysimeters for evapotranspiration and environmental measurements. Proceedings of the International Symposium on Lysimetry 1991 (July, 23-25), pp. 444. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration - Guidelines for Computing Crop Water Requirements - FAO Irrigation and Drainage Paper 56. FAO, Rome, Italy, pp. pp. 300. Balaghi, R., Badjeck, M.C., Bakari, D., De Pauw, E., De Wit, A., Defourny, P., Donato, S., Gommes, R., Jlibene, M., Ravelo, A.C., Sivakumar, M.V.K., 2010. Managing climatic risks for enhanced food security: key information capabilities. Procedia Environ. Sci. 1, 313–323. Blignaut, C., Taute, M., 2010. The development of a map showing the soybean production regions and surface areas of the RSA. University of Pretoria, Pretoria, South Africa accessed 01.09.16. http://purl.umn.edu/206510. Bréda, N.J., 2003. Ground‐based measurements of leaf area index: a review of methods, instruments and current controversies. J. Exp. Bot. 54, 2403–2417. Chen, J.M., Blanken, P.D., Black, T.A., Guilbeault, M., Chen, S., 1997. Radiation regime and canopy architecture in a boreal aspen forest. Agric. For. Meteorol. 86, 107–125. Cook, H.F., Valdes, G.S., Lee, H.C., 2006. Mulch effects on rainfall interception, soil physical characteristics and temperature under Zea mays L. Soil Till. Res. 91, 227–235. DAFF, 2016. Impact of Drought on Crop Production and the Food Value Chain. Department of Agriculture, Forestry and Fisheries. Republic of South Africa. Retrieved 10-08-2018 from. https://www.nda.agric.za/doaDev/sideMenu/others/ CCDM/docs/Impact%20of%20drought%20on%20crop%20production%20and %20food%20value%20chain%208%20July%202016.pdf. Dao, T.H., 1993. Tillage and winter wheat residue management effects on water infiltration and storage. Soil Sci. Soc. Am. J. 57, 1586–1595. Djaman, K., Irmak, S., 2013. Actual crop evapotranspiration and alfalfa-and grass-reference crop coefficients of maize under full and limited irrigation and rainfed conditions. J. Irrig. Drain. Eng. 139, 433–446. Dlamini, T.S., Tshabalala, P., Mutengwa, T., 2014. Soybeans production in South Africa. OCL 21, D207. https://doi.org/10.1051/ocl/2013047. Drexler, J.Z., Snyder, R.L., Spano, D., Paw U, K.T., 2004. A review of models and micrometeorological methods used to estimate wetland evapotranspiration. Hydrol.

71

Agricultural and Forest Meteorology 266–267 (2019) 65–72

N.C. Mbangiwa et al.

Porter, J.R., Jamieson, P.D., Wilson, D.R., 1993. Comparison of the wheat simulation models AFRCWHEAT2, CERES-Wheat and SWHEAT for non-limiting conditions of crop growth. Field Crop. Res. 33, 131–157. Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., 2009. AquaCrop the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron. J. 101, 438–447. Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., 2017. AquaCrop Reference Manual (version 6.0). Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., Valentini, R., 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob. Change Biol. Bioenergy 11, 1424–1439. Rost, S., Gerten, D., Hoff, H., Lucht, W., Falkenmark, M., Rockström, J., 2009. Global potential to increase crop production through water management in rainfed agriculture. Environ. Res. Lett. 4https://doi.org/10.1088/1748-9326/4/4/044002. pp. 044002. Rötter, R.P., Palosuo, T., Kersebaum, K.C., Angulo, C., Bindi, M., Ewert, F., Ferrise, R., Hlavinka, P., Moriondo, M., Nendel, C., Olesen, J.E., 2012. Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models. Field Crop. Res. 133, 23–36. Saab, M.T.A., Todorovic, M., Albrizio, R., 2015. Comparing AquaCrop and CropSyst models in simulating barley growth and yield under different water and nitrogen regimes. Does calibration year influence the performance of crop growth models? Agric. Water Manage. 147, 21–33. Sauer, T.J., Singer, J.W., Prueger, J.H., DeSutter, T.M., Hatfield, J.L., 2007. Radiation balance and evaporation partitioning in a narrow-row soybean canopy. Agric. For. Meteorol 145, 206–214. Savage, M.J., 2009. Estimation of evaporation using a dual-beam surface layer scintillometer and component energy balance measurements. Agric. For. Meteorol. 149, 501–517. Shao, C., Li, L., Dong, G., Chen, J., 2014. Spatial variation of net radiation and its contribution to energy balance closures in grassland ecosystems. Ecol. Process. 3, 1–11. https://doi.org/10.1186/2192-1709-3-7. Siebers, M.H., Yendrek, C.R., Drag, D., Locke, A.M., Rios Acosta, L., Leakey, A.D., Ainsworth, E.A., Bernacchi, C.J., Ort, D.R., 2015. Heat waves imposed during early pod development in soybean (Glycine max) cause significant yield loss despite a rapid recovery from oxidative stress. Glob. Change Biol. Bioenergy 21, 3114–3125. Singh, R., Singh, K., Bhandarkar, D.M., 2014. Estimation of water requirement for soybean (Glycine max) and wheat (Triticum aestivum) under vertisols of Madhya Pradesh. Indian J. Agr. Sci. 84, 190–197. Soil Classification Working Group, 1991. Soil Classification: a Taxonomic System for South Africa. Soil and Irrigation Research Institute, Department of Agricultural Development, Pretoria, South Africa, pp. pp. 257. Steduto, P., Hsiao, T.C., Raes, D., Fereres, E., 2009. AquaCrop - the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 101, 426–437. Suyker, A.E., Verma, S.B., 2009. Evapotranspiration of irrigated and rainfed maize –soybean cropping systems. Agric. For. Meteorol. 149, 443–452. Timouk, F., Kergoat, L., Mougin, E., Lloyd, C.R., Ceschia, E., Cohard, J.M., De Rosnay, P., Hiernaux, P., Demarez, V., Taylor, C.M., 2009. Response of surface energy balance to water regime and vegetation development in a Sahelian landscape. J. Hydrol. (Amst) 375, 178–189. Todorovic, M., Albrizio, R., Zivotic, L., Saab, M.T.A., Stöckle, C., Steduto, P., 2009. Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agron. J. 101, 509–521. Turmel, M.S., Speratti, A., Baudron, F., Verhulst, N., Govaerts, B., 2015. Crop residue management and soil health: a systems analysis. J. Agric. Food Syst. Commun. Dev. 134, 6–16. Twine, T.E., Kustas, W.P., Norman, J.M., Cook, D.R., Houser, P., Meyers, T.P., Prueger, J.H., Starks, P.J., Wesely, M.L., 2000. Correcting eddy-covariance flux underestimates over a grassland. Agric. For. Meteorol. 103, 279–300. Vanhie, M., Deen, W., Lauzon, J.D., Hooker, D.C., 2015. Effect of increasing levels of maize (Zea mays L.) residue on no-till soybean (Glycine max Merr.) in northern production regions: a review. Soil Till. Res. 150, 201–210. Vanuytrecht, E., Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., Heng, L.K., Vila, M.G., Moreno, P.M., 2014. AquaCrop: FAO’s crop water productivity and yield response model. Environ. Model. Softw. 62, 351–360. Weiss, M., Baret, F., Smith, G.J., Jonckheere, I., Coppin, P., 2004. Review of methods for in situ leaf area index (LAI) determination: part II. Estimation of LAI, errors and sampling. Agric. For. Meteorol. 121, 37–53. Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., 2002. Energy balance closure at FLUXNET sites. Agric. For. Meteorol. 113, 223–243. Zhang, Y., Liu, C., Tang, Y., Yang, Y., 2007. Trends in pan evaporation and reference and actual evapotranspiration across the Tibetan Plateau. J. Geophys. Res. 112, D12110. https://doi.org/10.1029/2006JD008161. Zwart, S.J., Bastiaanssen, W.G., 2004. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric. Water Manage. 69 115-13.

Process. 18, 2071–2101. FAO, 2018. FAOSTAT database. Food and Agriculture Organization of the United Nations. Rome. . Farahani, H.J., Izzi, G., Oweis, T.Y., 2009. Parameterization and evaluation of the AquaCrop model for full and deficit irrigated cotton. Agron. J. 101, 469–476. Fehr, W.R., Caviness, C.E., Burmood, D.T., Pennington, J.S., 1971. Stage of development descriptions for soybeans, Glycine max (L.) Merrill. Crop Sci. 11, 929–931. Fey, M.V., 2010. Soils of South Africa. Cambridge University Press, Cape Town, pp. pp. 287. Fisher, J.B., Melton, F., Middleton, E., Hain, C., Anderson, M., Allen, R., McCabe, M.F., Hook, S., Baldocchi, D., Townsend, P.A., Kilic, A., 2017. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626. Foken, T., Aubinet, M., Leuning, R., 2012. The Eddy covariance method. In: Aubinet, M., Vesala, T., Papale, D. (Eds.), 2012. Eddy Covariance: A Practical Guide to Measurement and Data Analysis. Springer, Heidelbeg, Germany, pp. pp. 1–19. Geddes, R.D., Scott, H.D., Oliver, L.R., 1979. Growth and water use by common cocklebur (Xanthium pensylvanicum) and soybeans (Glycine max) under field conditions. Weed Sci. 27, 206–212. Hati, K.M., Mandal, K.G., Misra, A.K., Ghosh, P.K., Bandyopadhyay, K.K., 2006. Effect of inorganic fertilizer and farmyard manure on soil physical properties, root distribution, and water-use efficiency of soybean in Vertisols of central India. Bioresour. Technol. Rep. 97, 2182–2188. Hernandez-Ramirez, G., Hatfield, J.L., Prueger, J.H., Sauer, T.J., 2010. Energy balance and turbulent flux partitioning in a corn–soybean rotation in the Midwestern US. Theor. Appl. Climatol. 100, 79–92. Jin, Z., Ainsworth, E.A., Leakey, A.D., Lobell, D.B., 2018. Increasing drought and diminishing benefits of elevated carbon dioxide for soybean yields across the US Midwest. Glob. Change Biol. Bioenergy 24, e522–e533. Katerji, N., Mastrorilli, M., Rana, G., 2008. Water use efficiency of crops cultivated in the Mediterranean region: review and analysis. Eur. J. Agron. 28, 493–507. Khojely, D.M., Ibrahim, S.E., Sapey, E., Han, T., 2018. History, current status, and prospects of soybean production and research in sub-Saharan Africa. Crop J. 6, 226–235. Klocke, N.L., Currie, R.S., Aiken, R.M., 2009. Soil water evaporation and crop residues. Trans. ASABE. 52, 103–110. Lal, M., Singh, K.K., Srinivasan, G., Rathore, L.S., Naidu, D., Tripathi, C.N., 1999. Growth and yield responses of soybean in Madhya Pradesh, India to climate variability and change. Agric. For. Meteorol. 93, 53–70. Liu, J., Williams, J.R., Zehnder, A.J., Yang, H., 2007. GEPIC–modelling wheat yield and crop water productivity with high resolution on a global scale. Agric. Syst. 94, 478–493. Mabhaudhi, T., Modi, A.T., Beletse, Y.G., 2014. Parameterization and testing of AquaCrop for a South African bambara groundnut landrace. Agron. J. 106, 243–251. McGloin, R., Šigut, L., Havránková, K., Dušek, J., Pavelka, M., Sedlák, P., 2018. Energy balance closure at a variety of ecosystems in Central Europe with contrasting topographies. Agric. For. Meteorol. 248, 418–431. Mengistu, M.G., Savage, M.J., 2010. Open water evaporation estimation for a small shallow reservoir in winter using surface renewal. J. Hydrol. (Amst) 380, 27–35. Mengistu, M.G., Everson, C.S., Moyo, N.C., Savage, M.J., 2014. The Validation of the Variables (Evaporation and Soil Moisture) in Hydrometeorological Models. Water Research Commission Report No. 2066/1/13. ISBN 978-1-4312-0514-1, Pretoria, South Africa. . Meyer, W.S., 2018. Increasing Water productivity in agriculture: an overview. In: Oweis, T. (Ed.), Water Management for Sustainable Agriculture. Burleigh Dodds Science Publishing Limited, Cambridge, UK, pp. pp. 1–25. Moyo, N.C., 2011. Seasonal Variation of Surface Energy Fluxes Above a Mixed Species and Spatially Homogeneous Grassland. MSc dissertation. University of KwaZuluNatal, Pietermaritzburg, South Africa, pp. pp. 105. Nave, W.R., Wax, L.M., 1971. Effect of weeds on soybean yield and harvesting efficiency. Weed Sci. 19, 533–535. Ngwenya, P., 2012. Herbaceous plant species richness and composition in moist Midlands mistbelt grasslands in KwaZulu-Natal: is there a relationship to veld condition? Afric. J. Range For. Sci. 29, 75–83. Nhamo, L., Mabhaudhi, T., Magombeyi, M., 2016. Improving water sustainability and food security through increased crop water productivity in Malawi. Water 8, 411. https://doi.org/10.3390/w8090411. Odhiambo, G.O., Savage, M.J., 2009. Sensible heat flux by surface layer scintillometry and eddy covariance over a mixed grassland community as affected by Bowen ratio and MOST formulations for unstable conditions. J. Hydrometeorol. 10, 479–492. Palosuo, T., Kersebaum, K.C., Angulo, C., Hlavinka, P., Moriondo, M., Olesen, J.E., Patil, R.H., Ruget, F., Rumbaur, C., Takáč, J., Trnka, M., Bindi, M., Çaldaǧ, B., Ewert, F., Ferrise, R., Mirschel, W., Şaylan, L., Šiška, B., Rötter, R., 2011. Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur. J. Agron. 35, 103–114. PANNAR, 2012. 2012 Product Catalogue. PANNAR SEED (PTY) LTD, P O Box 19, Greytown 3250, South Africa. pp. pp. 45. Perarnaud, V., Seguin, B., Malezieux, E., Deque, M., Loustau, D., 2005. Agrometeorological research and applications needed to prepare agriculture and forestry to 21st century climate change. In: Salinger, J., Sivakumar, M., Motha, R.P. (Eds.), Increasing Climate Variability and Change. Springer, Dordrecht, pp. pp. 319–340.

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