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Water SA

versión On-line ISSN 1816-7950
versión impresa ISSN 0378-4738

Water SA vol.44 no.4 Pretoria oct. 2018

http://dx.doi.org/10.4314/wsa.v44i4.02 

ORIGINAL ARTICLES

 

Model parameters of four important vegetable crops for improved water use and yield estimation

 

 

JT VahrmeijerI, III, *; JG AnnandaleI; JM SteynI; KL BristowI, II, IV

IDepartment of Plant and Soil Sciences, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa
IIUniversity of Pretoria Water Institute, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa
IIICitrus Research International, PO Box 28, Nelspruit 1200, South Africa
IVCSIRO Agriculture and Food, PMB Aitkenvale, Townsville, QLD 4814, Australia

 

 


ABSTRACT

High-value vegetable crops are typically grown under irrigation to reduce production risk. For water resource planning it is essential to be able to accurately estimate water use of irrigated crops under a wide range of climatic conditions. Crop water use models provide a means to make water use and yield estimates, but need crop- and even cultivar-specific parameters. There is generally a lack of crop-specific model parameters for some important commercially grown vegetable crops, especially parameters determined over both summer and winter seasons. The experimental site used in this study was on the Steenkoppies Aquifer, a catchment under stress and an important vegetable production area in South Africa. Crop-specific growth parameters and water use for 4 selected high-value vegetable crops (beetroot, cabbage, carrots and broccoli) were measured over multiple seasons (two summers and one winter). These were used to parameterise the Soil Water Balance (SWB) generic crop growth model for both summer and winter seasons. In seasons where the same cultivar was planted, a single set of model parameters could be used to successfully simulate crop growth and water use. Results show that the amount of irrigation water required is dependent on season and rainfall, with broccoli having the lowest (1.8-2.7 kg m3) and beetroot the highest (12.2-23.4 kg m3) water productivity (WPFM), defined as fresh mass of marketable product per unit water consumed. The root crops had a greater harvest index (HIDM) than cabbage and broccoli. The parameters obtained expand the current database of SWB crop growth parameters for vegetables and can be used in a wide range of mechanistic simulation models to improve water management at field and catchment levels.

Keywords: SWB model, Steenkoppies Aquifer, carrot, broccoli, beetroot, cabbage


 

 

INTRODUCTION

Implementing crop models for analysing the processes linking weather and soil conditions to crop growth and water use are valuable aids for planning and for making management decisions at different spatial scales (field or catchment), time periods (short and long term) and for different climatic conditions (average and extreme). Such models also provide information on resource dynamics and have the capability of predicting future events for different scenarios (Singels et al., 2010). Jones (2013) distinguishes between descriptive (more empirical) and explanatory (more quantitative) models. Boote et al. (2010) groups potential uses of crop models into 5 primary categories: (i) synthesis of research knowledge, (ii) crop system decision management, (iii) policy analysis, (iv) real-time decision support and (v) education purposes. Different aspects of the soil-plant-atmosphere continuum are emphasized with different modelling approaches and with ultimate model objective. For example, Ranatunga et al. (2008) reviewed models with the emphasis on soil water, while Jones (2013) considered modelling of plant water relations and irrigation of horticultural crops. Steduto et al. (2009) explains the concepts and principles of modelling biomass production and yield, whilst Stöckle et al. (2014) described simulation models for multi-crop, multi-year cropping systems.

The history of the development of models for crop growth and crop water relations in South Africa was reviewed by Singels et al. (2010) and approaches for irrigation scheduling by Annandale et al. (2011). A mechanistic crop growth model, called the Soil Water Balance (SWB) model, was developed in South Africa and is well-established and widely used (Annandale et al., 1999; Jovanovic and Annandale, 1999; Jovanovic et al., 1999; Jovanovic and Annandale, 2000; Jovanovic et al., 2000; du Sautoy et al., 2003; Annandale et al., 2004; Beletse et al., 2008; Singels et al., 2010; Fessehazion et al., 2014; Tesfamariam et al., 2015). SWB was developed as a real-time, generic crop growth, soil water balance, irrigation scheduling model (Annandale et al., 1999; Annandale et al., 2011). For SWB and indeed other mechanistic models to simulate crop growth and water use, crop-specific model parameters are required. SWB predicts the water balance and crop growth from weather, soil and crop data. Pennman-Monteith grass reference daily evapotranspiration (ETo) in mm (Pereira et al., 2015) is calculated from weather data, and thermal time is used to describe crop development. Soil water movement is simulated using a layered cascading model, where each layer can have its own physical properties. Rainfall interception, drainage and runoff are also accounted for in the model. Dry mass production is calculated after crop emergence and until maturity on a daily basis as either transpiration- or radiation-limited (Goudriaan and Van Laar, 2012), and assimilates are partitioned to different plant organs depending on phenological stage and water stress level.

Vegetables are seen as high-value crops, ranking amongst the top 5 agricultural commodities in monetary terms worldwide (FAO, 2017). In South Africa, vegetables collectively rank 7th in importance for crops produced under irrigation (Backeberg et al., 1996). Vegetables represent a wide range of crops that differ greatly in growth pattern, plant structure and even cultivation methods and generally require irrigation for optimum yield. A better understanding of crop water use is needed to optimise irrigation water management at field level for irrigation system design, to effect water and energy savings, for salt management (Jovanovic et al., 1999; Beletse et al., 2008) and to facilitate water management at catchment level, for example, water allocations during periods of low rainfall. However, there is a lack of information on crop-specific parameters for vegetables in general (Jovanovic et al., 1999). This is particularly true for commercially grown vegetables in both warm and cool seasons. Therefore, the objectives of this study were to measure, over multiple seasons, seasonal growth and water use of selected important vegetable crops commercially grown on the Highveld (approximate altitude 1 700 m with temperate climate), and to establish crop-specific growth parameters for summer and winter cropping to be used in mechanistic simulation growth models. Such growth models can then be used to estimate, for example, water abstraction from groundwater resources for the allocation and management of water use at farm or catchment level. This is urgently required in the case of the Steenkoppies Aquifer, and no doubt in many other catchments under stress, where careful management of water resources is required.

This study formed part of a larger study (Reinders et al., 2010), to determine irrigation water use efficiency. Field trials that reflect the crops, cultivars and local practices were conducted on the Steenkoppies Aquifer. The study was conducted over 3 seasons to fall within the time constraints of the larger study.

 

MATERIAL AND METHODS

Experimental set-up

Field trials were carried out at Rosaly Boerdery (26°04 S and 27°36 E, 1 678 m amsl), a Global GAP-audited (good agricultural practices) farming enterprise on the Steenkoppies Aquifer in Tarlton, Gauteng, South Africa. The experiment was conducted on one half of a 24 ha centre pivot, which was divided into five adjacent 2 ha experimental fields. The area not used for the trial was planted with vegetables for commercial production. Soil preparation reflected local practices and involved ploughing, rotovating and the forming of 1.2 m wide raised beds with 3 rows of vegetables planted on each raised bed. Four vegetable species, which are commonly grown on the Steenkoppies Aquifer, were monitored over two summer (October to March) and one winter (April to September) season. 'Tenacity' which is a warm season, round head cabbage hybrid was planted in summer and 'Grandslam' a medium maturing winter hybrid with a round head that flattens at the base at full maturity, in the winter season. Both cultivars belong to the white cabbage group (Brassica oleracea L. var. capitata), are large framed with a semi-erect growth pattern and are commonly grown for the fresh market. In summer, an early-maturing Nantes carrot (Daucus carota L.) hybrid cultivar ('Star 3006') with medium to long leaves that grow upright was planted, and in winter, a medium to early Nantes hybrid cultivar ('Dordogne') with vigorous long leaves was grown. The same beetroot cultivar (Beta vulgaris 'Red Ace'), was planted in both the summer and winter trials. Two broccoli cultivars belonging to different groups were planted, 'Star 2204' (Brassica oleracea L. var. italica) in summer and 'Parthenon' (Brassica oleracea L. var. cymosa) in winter.

The cultivars, planting and harvest dates, plant densities and growth periods are summarised in Table 1. Apparent inconsistencies between crops in their growth periods in days and day degrees, are due to differences in their base temperatures. The between-row spacing for the different vegetable crops was 0.4 m. In-row spacing of the cabbage seedlings was 0.44 m and 0.35 m for the broccoli seedlings, for both the summer and winter seasons. Trial plots (4 replicates of 30 m2 per crop) were randomly chosen and clearly marked within the experimental fields. Soil samples were taken and analysed to determine fertiliser requirements. Each experimental field was fertilised and managed according to its specific needs. Pre- and post-emergence herbicides were applied for weed control. Spraying for pests occurred only when required.

Climatic data, both historical and for the period of these trials, was obtained from an automatic weather station at Deodar (26°08 S, 27°35 E, 1700 m altitude) that is owned and maintained by the local Department of Agriculture, 7.5 km from the study site. A summary of climatic data for the experimental periods is presented in Table 2. For the 2008 summer season (2008/09/16-2008/12/22) the experimental period had a higher average vapour pressure deficit (VPD) than for the 2008 winter and previous summer experimental periods. As expected, the average radiation and temperatures were lower for the 2008 winter experimental period, than for the two summer periods (Table 2).

A profile pit was dug at the experimental site and soil samples were taken at 0.15 m intervals to a depth of 0.6 m to determine bulk density (ρb), soil texture and volumetric soil water content (ϑ) at field capacity (FC) and permanent wilting point (PWP). The ρb of the soils was determined by gently tapping a cylinder of known volume horizontally into the side of the profile pit. The soil was removed and dried at 105°C for 24 h. Volumetric water content at FC was determined, before planting, by saturating a portion of the field which was then left for 48 h to drain before sampling, while PWP was determined at the end of the season by withholding irrigation on a section of the experimental plot until the plants died. Soil samples of a known volume were taken and ϑ was calculated from the mass loss before and after the soil samples were dried at 105°C for 24 h. Bulk density varied from 1.42-1.54 Mg·m3 for the 0-60 cm soil profile (Table 3). Clay content varied from 9.8-13.3%, θ at FC from 0.147-0.215 m3·m3 and PWP from 0.067-0.088 m3·m3 (Table 3). The soils of the experimental site have a textural class of a loamy sand and are classified as a Bainsvlei soil form (Soil Classification Working Group, 1991).

Field measurements

Volumetric soil-water content (ϑ) was measured at least once a week with a calibrated neutron water meter. Readings were taken in the middle of each trial plot at 3 positions, one within a plant row and on either side of the plant row at 0.15 m depth increments down to 0.6 m. The soil-water deficit to field capacity was calculated for each layer and the average for the three different positions determined. Rain gauges were installed to measure irrigation (I) and precipitation (P). Infiltration rate was measured according to the method described by Reinders and Louw (1984) and ranged between 17 mm·h1 and 36 mm·h1 on a 30 min test run. Due to the high infiltrability and flat topography of the field, runoff (R) was assumed to be negligible.

Plant material for growth analyses was harvested from four 1 m2 plots at least once every fortnight and divided into leaves, stems, roots and harvestable components (underground storage organs for carrots and beetroot, and edible vegetative and reproductive portions of cabbage and broccoli). Leaf area index (LAI) was calculated from photosynthetically active leaf area measured with a belt-driven leaf area meter (LiCor, Lincoln, Nebraska, USA). For cabbage and broccoli, the outer green leaves were taken as photosynthetically active. Total and harvestable fresh mass was determined directly after harvest, whereafter the plant material was dried in an oven at 60°C until constant mass to determine dry mass of the different plant components. Fractional interception (FI) of photosynthetically active radiation (PAR, 400-700 nm) was determined periodically throughout the season. For field measurements, 10 pairs of readings for each vegetable crop, one above and one below the crop canopy, were taken in rapid succession with a sunfleck ceptometer (Decagon, Pullman, USA) to estimate canopy cover.

Modelling parameters

Seasonal evapotranspiration (ET) was calculated using the soil water balance equation:

where I is irrigation, P is precipitation, R is runoff, D is drainage and ΔS is the change in soil-water-storage for the 0.6 m soil profile. Drainage was estimated with the SWB model once parameterised for crop growth, and ΔS was calculated from soil water content measurements at the beginning and end of the season.

The SWB model uses a mechanistic approach to model crop growth and requires, among others, the following parameters for each crop:

i) Canopy extinction coefficient (Ks) for total solar radiation

ii) Dry matter: Transpiration ratio, adjusted for vapour pressure deficit (DWR, kg·kg1·Pa)

iii) Radiation-use efficiency (RUE, kg·MJ1)

iv) Leaf-stem dry matter partitioning (p, m2·kg1)

v) Specific leaf area (SLA, m2·kg1)

vi) Growing day degrees (GDD, d°C) required to reach different development stages

vii) Maximum root depth (RDmax, m)

The canopy extinction coefficient (KPAR), measured with a ceptometer, is for photosynthetically active radiation (PAR) and is used in some models to calculate photosynthesis as a function of intercepted PAR. KPAR is a crop-specific parameter where the fraction of PAR intercepted (FIPAR) is well correlated with LAI (Goudriaan and Van Laar, 2012) and canopy architecture (Campbell and Norman, 1998). KPAR is calculated from the exponential relationship between field measurements of FIPAR and LAI (Campbell and Van Evert, 1994):

However, the canopy extinction coefficient for total solar radiation (Ks) is used in SWB to determine radiation-limited dry matter production (Goudriaan and Van Laar, 2012) and for partitioning of ET into evaporation and crop transpiration (Ritchie, 1972). Therefore, measured KPAR was converted to Ks according to the procedure recommended by Campbell and Van Evert (1994) as described by Jovanovic et al. (1999).

Water vapour and carbon dioxide (CO2) both diffuse through the stomata of leaves and, therefore, the assimilation of carbon is accompanied by transpirational water loss that can be described by a simple gas exchange dry matter production model (Campbell and Norman, 1998):

where DM is total dry matter (kg m2), which includes all above-ground canopy dry mass (CDM) and, in the case of root crops, the underground harvestable storage organ. Tr is transpiration (mm), VPD is the vapour pressure deficit (Pa) and the slope of the relation between DM and water use, DWR (dry matter water ratio), is the water productivity (Van Halsema and Vincent, 2012) of the harvestable dry matter (WPDM) per unit of water consumed (ET) adjusted for VPD. Due to the difficulties in determining transpiration and total dry matter, ET (not Tr) and above-ground and harvestable root mass (not total DM) were used to estimate a lower limit value of DWR.

Dry matter production, under conditions of radiation-limited growth, is modelled as a function of the daily cumulative product of fractional interception (FIRAD) and incoming solar radiation (Rs), after Goudriaan and Van Laar (2012):

RUE is the slope of the regression between intercepted solar radiation and DM production, forced through the origin. Daily increments in DM are calculated either as transpiration-limited (Eq. 3) or radiation-limited (Eq. 4). Dry matter is preferentially partitioned to reproductive sinks and roots, and remaining DM is partitioned to leaves and stems (CDM). Specific leaf area (SLA), calculated as the ratio between leaf area and leaf dry matter, is taken as the seasonal average for each crop, but caution should be taken with the use of this parameter because SLA tends to decrease during the season (Jovanovic et al., 1999). The leaf-stem dry matter partitioning (p) is a function of SLA, LAI and CDM and the lower the value of p, the greater the apportionment of biomass to leaves.

Growing day degrees (GDD) was determined from daily average air temperature after Campbell and Norman (1998):

where the unit for GDD is d°C, Tx is the daily