Forage chicory model: Development and evaluation

Field Crops Research 246 (2020) 107633

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Forage chicory model: Development and evaluation a, ,1

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Rogerio Cichota * , Russell McAuliffe , Julia Lee , Elena Minnee , Kirsty Martin , Hamish E. Brownb, Derrick J. Moote, Val O. Snowe a

AgResearch Limited, Lincoln Research Centre, Christchurch, New Zealand The New Zealand Institute for Plant and Food Research Limited, Lincoln, New Zealand DairyNZ, Hamilton, New Zealand d Dairy Farm Management Services, Darfield, New Zealand e Faculty of Agriculture and Life Science, Lincoln University, Canterbury, New Zealand b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Pastoral systems APSIM Forage yield Biomass accumulation Nitrogen uptake Cichorium intybus L.

Chicory has been promoted as an alternative forage crop for livestock farming. It can produce large biomass yields and is highly palatable to animals. However, managing forage chicory in pastoral farms can be challenging because of its growth pattern and low persistence. There is thus a need for modelling tools that can help to understand and manage forage chicory within the farm system. In this work we review the main botanical characteristics of the chicory plant and describe a model developed to simulate the phenology and biomass accumulation of chicory as forage. The model was developed using the Plant Model Framework and it is available for use within the Agricultural Production Systems Simulator. Four experiments with treatments including the use of irrigation, different N fertiliser rates, and defoliation regimes, were used to test the model. We show that the model was able to simulate the biomass accumulation pattern, with R2 values of 0.40-0.54 for standing above-ground biomass and 0.64-0.89 for cumulative harvested material. The performance was weaker when describing plant nitrogen, with R2 of 0.20-0.25 for N content in the above-ground biomass. This indicates that care must be taken when using the model to simulate N balance, and that model refinements are required. For this, more information is needed on the partitioning and mobilisation of N amongst the various organs and on how biomass allocation changes across seasons.

1. Introduction Chicory (Cichorium intybus L.) is a perennial herb of the family Asteraceae native to Eurasia that can now be found at mid latitudes worldwide (Bremer and Anderberg, 1994; Li and Kemp, 2005). In many countries wild chicory is considered a weed, although domesticated varieties have been used for centuries. Cultivated varieties have been selected to produce primarily either leaf or taproots for human consumption (Jung et al., 1996; Barcaccia et al., 2016). More recently, cultivars have also been selected, primarily from wild chicory, to be used as livestock forage. Initial development has been mainly conducted in New Zealand for use in grazing systems (Hare et al., 1987; Li and Kemp, 2005), but forage chicory is now used in a variety of livestock systems around the world (Alloush et al., 2003; Gentile et al., 2003; Li et al., 2010; Pirhofer-Walzl et al., 2011). Forage chicory can grow fast under favourable condition in spring/ summer, producing large amounts of biomass, and is highly palatable to

ruminants (Clark et al., 1990b; Jung et al., 1996; Alemseged et al., 2003; Labreveux et al., 2004). However, when vernalised, chicory plants produce stems which reduce the intake by grazing animals considerably, and specific management is required to maintain the quality of the sward (Clark et al., 1990b; Li et al., 1997b; Clapham et al., 2001; Li and Kemp, 2005). The combination of high growth rates in spring/summer and low rates over winter makes it difficult to select companion species for chicory to balance feed supply year round (Hume et al., 1995; Belesky et al., 2000; Kemp et al., 2002). These management issues may be responsible for the reduced adoption of forage chicory by farmers. Nevertheless, it remains an attractive forage alternative capable of producing high annual yields. Reported values from pure chicory swards range from 16 to 19 t dry matter (DM) ha−1 year−1 in dryland conditions and up to 22.6 t DM ha−1 year−1 in irrigated swards (Brown and Moot, 2004; Neal et al., 2009). Forage chicory can also be used in mixed pastures (Moloney and Milne, 1993; Belesky et al., 1999; Totty et al., 2013; Martin et al., 2017).

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Corresponding author. E-mail address: [email protected] (R. Cichota). 1 Current address: The New Zealand Institute for Plant and Food Research Limited, Lincoln, New Zealand. https://doi.org/10.1016/j.fcr.2019.107633 Received 19 October 2018; Received in revised form 30 August 2019; Accepted 20 September 2019 0378-4290/ © 2019 Elsevier B.V. All rights reserved.

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environmental conditions, with an extensive publication record (documentation and a comprehensive list of references can be found in www.apsim.info). APSIM provides a robust platform for the development of crop models. It already contains models for soil water and N processes as well as the infrastructure to handle weather data and to simulate management actions, thus enabling the developer to focus on describing the crop. It also offers the Plant Modelling Framework (PMF), which is the main tool for building plant models in APSIM next generation (Brown et al., 2014, 2018; Holzworth et al., 2018). PMF’s main features include its modular nature and the externalisation of the model structure and parameterisation. This allows the development of plant models using a graphical user interface and enables the design of models with different degrees of complexity. A basic description of how PMF works is provided here along with the description of the chicory model. More details on the PMF design and usage can be found elsewhere (Brown et al., 2014; www.appsim.info; Brown et al., 2018). It is important to note that the PMF components are abstractions of processes at plant or sub-plant levels. However, crop models are typically designed to represent a community of plants. This implies that plant-to-plant variations, or even in-field spatial variability, are not explicitly captured by PMF models (although APSIM allows for the simulation of multiple independent locations as an alternative for describing spatial variability). Population dynamics are currently not simulated by the model, and interactions with other plants (e.g. under multi-cropping) are accounted for by relatively simple approaches. Competition for resources (i.e. light, water and nutrients) between different crops are controlled by other models within APSIM (MicroClimate and SoilArbitrator), which still need testing and development. Any model developed using PMF should be able to be readily incorporated into APSIM simulations. Sanctioned, peer-reviewed models, such as the Chicory model, are available in the ‘crops’ toolbox in APSIM’s User Interface, from which it can be added to a ‘Field’ in the simulation. The user has to provide, beyond the basic weather and soil data, the appropriate plant-soil parameters in the soil node. Manager scripts are then used to control the management actions (sowing, irrigation, harvests, etc.).

There has been interest for some time in manipulating sward biodiversity to add plant traits not common in typical sward mixes (e.g. Tilman et al., 1996). The benefits of diverse pasture include improving feed quality, enhancing water and nutrients use efficiency, conferring drought resistance, and increasing tolerance to pests. Recently, the focus has been on using pasture biodiversity to reduce environmental impacts of grazing systems while maintaining or increasing productivity (Woodward et al., 2012; Sanderson et al., 2013; Totty et al., 2013; Vogeler et al., 2017). Loss of water quality due to nitrogen (N) leaching is of major concern for intensive livestock farming because urine deposited by grazing animals is the main driver for N losses in these systems (Cuttle et al., 2001; Di and Cameron, 2002; Cichota et al., 2012). The proposition is that by manipulating species composition in the sward it is possible to reduce the overall N intake by livestock and/ or improve N utilisation, thus reducing excretion (Beukes et al., 2014; Vibart et al., 2016). A more diverse sward may also be able to utilise more of the N eventually deposited in to the soil. Several ongoing research programmes are aiming to determine the effectiveness of this practice (e.g. Carlton et al., 2017; Vogeler et al., 2017). It is also necessary to refine guidelines to implement and manage diverse pasture for different farming systems and environmental conditions. Regulations are being considered or beginning to be implemented worldwide, especially in areas with intensive land-use (e.g. Langeveld et al., 2007; Doole et al., 2013; Monaghan and de Klein, 2014). These regulations aim to reduce the environmental impact of nutrient losses, and can focus either on limiting inputs or on attempting to reduce losses. The latter approach allows for more freedom of management and should limit production less. However, the implementation of such an approach requires ways to determine the effect of different management options and to monitor their outcome. Modelling tools, supported by measurement and monitoring sites, have been proposed as a means to accomplish this (Öborn et al., 2003; Cichota and Snow, 2008; Monaghan and de Klein, 2014). Computer models can also help to devise and evaluate management options that aim to utilise resources efficiently and increase production. The tools required by farm managers need to be fast and easy to use, thus relatively simple (Gourley et al., 2007; Cichota and Snow, 2008; del Prado et al., 2011). However, their development often require initially more complex approaches; these enable in-depth study of options and consequences and can then be simplified into management actions. This is the role of processbased, systems models (Snow et al., 2014; Antle et al., 2017). Such models rely less on calibration and thus can be used to extrapolate analyses to a wider range of systems and environmental conditions. The Agricultural Production System Simulator (APSIM) is a modelling framework that has been increasingly used to study agricultural systems in New Zealand and worldwide (Holzworth et al., 2014; Snow et al., 2014; Holzworth et al., 2018). A limitation for using APSIM can be the restricted number of plant species available. However, the number of models is continuously increasing, as this open-source framework is under active ongoing development. Recently, the Plant Modelling Framework (PMF; Brown et al., 2014) has been included to APSIM next generation to facilitate the development of plant models. The objective of this work was to develop a model for describing phenology and biomass accumulation of forage chicory that can be used within the APSIM next generation modelling framework. The model was devised based on the botanical information of chicory available in the literature (phenology, physiology, etc.), with a focus on biomass production of forage cultivars. A summary of this review and the description of the forage chicory model using the PMF is presented, followed by its evaluation against data from four field experiments.

1.2. The chicory plant Chicory plants have large hairless leaves growing from a basal rosette; these are produced continuously and with regular average mature size (40-60 cm2) throughout the growing season (Clapham et al., 2001; Alloush et al., 2003; Devacht et al., 2009; Mathieu et al., 2014). Stem development is related to reproductive growth and occurs only after the chicory plants have been vernalised (Moloney and Milne, 1993; Li et al., 1994; Gianquinto, 1997). A primary stem sprouts from the main rosette, while secondary ones can grow if the main stem is damaged (after grazing, for instance). These have relatively small leaves (around 10 cm2) and can grow to heights greater than 1 m (Rumball, 1986; Li et al., 1998; Clapham et al., 2001). The growth of stems can be inhibited by defoliation, but they can still make up 15 to 50% of the biomass in pure forage chicory swards during summer. Chicory produces composite blue flowers at the top of stem and branches during the summer months and are open for one day only (Clapham et al., 2001). Undefoliated plants can produce more than 200 flowers per plant in a season (Clapham et al., 2001). Seeds mature fast, some 20–40 days after pollination, and consequently a chicory plant can have mature seeds while still developing new flowers on upper nodes (Hare et al., 1990; Clapham et al., 2001). Chicory seeds have a dark brown colour when ripe and weigh between 1.4 and 1.7 g per 1000 seeds (Hare, 1986; Reed et al., 2008). Reseeding is possible, but it is generally considered unlikely that young plants would survive competition with fully grown chicory plants and weeds (Li et al., 1997b; Alemseged et al., 2003). The chicory inflorescence has high feed quality and will be grazed preferentially by ruminants (Clark et al., 1990b; Arias-Carbajal, 1994).

1.1. Introduction to APSIM and PMF The APSIM model is a modular framework designed to simulate long-term dynamics in agricultural systems (Keating et al., 2003; Holzworth et al., 2014). It has been used in a variety of systems and 2

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available data, or given preference to those results based on ‘Grasslands Puna’ cultivar, from which most data on forage chicory was obtained. The model and documentation are also available online in the Apsim website (www.apsim.info).

Chicory has a thick deep taproot which is crucial for its persistence and drought tolerance (Hume et al., 1995; Li and Kemp, 2005; Cranston et al., 2015). Roots usually reach depths beyond 1 m and up to 2 m, although most of the root mass is in the top 20 cm (Brown et al., 2003; Gentile et al., 2003; Sapkota et al., 2012). The taproots build up reserves of sugars (such as mono- and di-saccharides and especially fructans) during the growing season that can be used to supplement demand when photosynthesis is limited. These reserves can make up 60–85% of taproots mass in root cultivars (Améziane et al., 1995; Ernst et al., 1995; Demeulemeester et al., 1998; Monti et al., 2005) but about only 20–30% in forage cultivars under defoliation (Ernst et al., 1995; Li et al., 1997a; Quijada, 2015). Drought tolerance in chicory has been attributed to its deep rooting system (Brown et al., 2003; Skinner, 2008; Hayes et al., 2010) as well as to its carbohydrate reserves stored in the taproots (Li and Kemp, 2005; Langworthy et al., 2015). The reserves seem to be more important for growth in spring and have also been linked to plant survival over winter (Li et al., 1997c; Li and Kemp, 2005). Chicory can be sown in spring or autumn, when temperatures are above 10 °C, and weed control is necessary to ensure establishment (Moloney and Milne, 1993; Moot et al., 2000; Li and Kemp, 2005). Optimum pH for chicory growth is 5.5–6.0 (Crush and Evans, 1990). There is little direct information on nutrient requirements of forage chicory, with recommendations given based on pastures of similar productivity (e.g. Moloney and Milne, 1993; Upjohn et al., 2002). Forage chicory is a moderately persistent herb under grazing conditions. Population decline can be temporarily compensated as the initial rosette splits into multi-crowns after the first growing season (Moloney and Milne, 1993; Clapham et al., 2001; Li and Kemp, 2005). Forage chicory is considered to have high tolerance to insect pests, but it is susceptible to fungal root diseases (Hare et al., 1990; Moloney and Milne, 1993; Li and Kemp, 2005). Root diseases are cited as a major factor that reduces persistence of chicory in swards, but poor plant vigour due to excessive defoliation may be responsible for its susceptibility to fungal infection (Li and Kemp, 2005). Chicory is considered to be highly nutritious for animals (Rumball, 1986; Belesky et al., 2001; Li and Kemp, 2005; Pirhofer-Walzl et al., 2011) and, provided that stem growth is kept under control, the nutritional value is consistent over the growing season (Labreveux et al., 2006). As a fast-growing, N-responsive plant, chicory can cause nitrate poisoning in animals when grown under high N fertiliser regimes (Moloney and Milne, 1993; Belesky et al., 2000). It also has low tannin content, which indicates it could cause bloating in animals grazing on pure swards. However, chicory is considered bloat-safe, possibly because of high rumen outflow and rumen pH (Moloney and Milne, 1993; Barry, 1998; Li and Kemp, 2005). Anthelmintic properties of chicory have been associated with the presence of sesquiterpene lactones compounds, although they can also cause milk tainting (Rumball, 1986; Barry, 1998; Foster et al., 2011). Thus, the recommendation is to use cultivars with low sesquiterpene lactones content and/or limit the proportion of chicory in the feed for lactating dairy cows (Li and Kemp, 2005; Lee et al., 2015). Animals fed with chicory have shown liveweight gains equal to or greater than those on perennial ryegrass/white clover pastures (Clark et al., 1990b; Turner et al., 1999; Li and Kemp, 2005). The milk solids production of dairy cows has also improved when chicory was included in their diet (Chapman et al., 2008; Minneé et al., 2012).

2.1.1. Phenology phases 2.1.1.1. Germinating. Chicory seeds will germinate in the model at the day of sowing, provided that the soil moisture is greater than the wilting point, which is a default behaviour of PMF. Alternatively, the user can define the number of days required for germination (by setting the parameter ‘DaysFromSowingToEndPhase’). 2.1.1.2. Emerging. This is the period when the seed develops into a seedling and emerges from the soil. It is defined by a fixed thermal time (‘lag’) in which only transformations in the seed occur. This is followed by a period with shoot elongation that lasts until the seedling reaches the surface and photosynthesis commences. Available data suggest that chicory emergence requires 150–200 °C d of thermal time (Amaducci and Pritoni, 1998; Moot et al., 2000; Clapham et al., 2001; Monti et al., 2005). The current model uses a lag of 75 °Cd and an elongation rate of 10 °Cd/mm. Currently the model uses air temperature, but this will be changed to soil temperature when APSIM simulations will incorporate soil temperature. Emergence rate, or the survival of seedlings, has been shown to be affected by depth of sowing (Peri et al., 2000; Sanderson and Elwinger, 2000a), with only a few seedlings emerging when seeds were sown below 50 mm. Seedling survival is not explicitly simulated by the model and thus should be controlled by the user by changing the sowing density if required. 2.1.1.3. Vegetative. This phase starts when plants emerge and continues until the plants are vernalised. Vernalisation in chicory occurs at midto-low temperatures, ranging between 0 and 12 °C, and the requirements for full vernalisation seem to be cultivar-specific and/or have some interaction with photoperiod (Wiebe, 1989; Gianquinto, 1997; Dielen et al., 2005). Moreover, not all plants reach the reproductive phase every year; for instance, forage chicory (‘Puna’) grown in field conditions in the USA, had about 50–60% of plants reaching the reproductive stage after winter (Clapham et al., 2001; Sanderson et al., 2003). And plants that bolted in one year tended to remain vegetative in the following year and vice versa. The model accommodates this by assuming that the chicory population requires a relatively low amount of vernalisation (10 vernalising degrees-days, VDD) to trigger the end of vegetative phase. The intensity of the reproductive growth, that is, the shift in biomass allocation towards stems and inflorescence, is then controlled by the extent of vernalisation beyond that minimum. Reversal of vernalisation, or devernalisation, occurs with temperatures greater than 15–20 °C, with higher temperatures having a greater effect (Chouard, 1960; Wiebe, 1990; Gianquinto, 1997). During this phase only leaves and root/ taproots can grow. 2.1.1.4. Inductive. After the minimum vernalisation, chicory plants wait for a photoperiod signal to start their reproductive growth (Wiebe, 1990; Moloney and Milne, 1993). Reported data indicate that bolting only occurs when daylight is greater than 12 h and flowers sprout with approximately 14 h of daylight (Hare, 1986; Gianquinto, 1997; Clapham et al., 2001). Based on these trends, the end of the inductive phase in the model is triggered by the accumulation of 20 days with a photoperiod above 12.5 h. Vernalisation is still accumulated during this phase and biomass allocation is essentially the same as in the vegetative phase. Only at the end of the inductive phase does the allocation of biomass shift towards above-ground organs.

2. Material and methods 2.1. Model description and parameterisation Following the scheme proposed by PMF (Brown et al., 2014, 2018) and based on the description of the plant given above, the chicory model was structured as described below. The majority of parameter values were taken from published results, typically using the average of

2.1.1.5. Stem elongation. This is the first stage of reproductive growth. It is characterised by a significant shift in biomass allocation, with 3

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growth of leaves and roots being largely suppressed in favour of that of stems. Stem growth decelerates when flower buds start to appear, but only ceases when the reproductive phase ends. Available data and growth pattern suggest that this phase lasts for about 800 °C d (Hare et al., 1987, 1990; Clapham et al., 2001), and this is also used in the model. 2.1.1.6. Reproductive. In this phase biomass will be preferably allocated to flowers and seeds, but both leaves and stems can still grow, especially after a defoliation. Flowering of chicory is spread over approximately two months and overlaps with seed development and ripening. This is why the model does not separate these phases. Available data suggest that approximately 500 °C d are needed for flower buds to develop until they can be pollinated, and another 300 °C d for seeds to ripen after pollination (Hare, 1986; Clapham et al., 2001). Once seed ripening begins, flower formation rate decreases and ceases soon after the day-length decreases below approximately 13 h. In the model, the duration of this phase is defined by the accumulation of 800 °C d. At the end of this phase, vernalisation is reset and the plants return to the vegetative phase.

Fig. 2. Effectiveness of vernalisation (dashed line) and de-vernalisation (solid line) as function of mean air temperature as used in forage chicory.

1997). Default values for vernalisation effectiveness used in the model are shown in Fig. 2. 2.1.4. Organs Plants in PMF are composed of several classes representing organs. Each class holds the organ’s state information, such as biomass and N contents, and is responsible for controlling the biomass flow processes, such as demand and senescence (see Brown et al. (2014) for more details). The biomass in each organ is composed of three fractions: structural, metabolic, and storage. Storage biomass can be re-translocated and used for growing new tissue, while structural cannot be reused in the plant. In the chicory model, only leaves have the metabolic component, which is treated as structural in general, except it can be reallocated upon senescence. Biomass not reallocated is detached after senescence and is added to surface or soil organic matter pools. Three thresholds define how N is partitioned in the plant: the minimum N concentration (Nmin ) is that of structural tissues and thus the concentration of dead material; the critical N concentration (Ncrit ) sets the amount of N needed for plant functioning but does not limit growth; the maximum N concentration (Nmax ) represents the upper limit for N storage; the N amount above Ncrit represents luxury uptake and can be remobilised for plant growth; the N above Nmin and below Ncrit is made available only upon organ senescence. In PMF, the release of non-structural biomass from each organ is controlled by a senescence rate (d−1), which is further affected by temperature and water availability in the forage chicory model. Each organ has a series of parameters defining it and controlling its processes, a brief summary of the most relevant is given alongside the description of each organ.

2.1.2. Cardinal temperatures Thermal time is used to define phenological changes; it is computed on a daily basis based on the plant cardinal temperatures. These are assumed in the model to be the same for all phenological phases. The optimum temperature for chicory growth is reported to be around 20–25 °C (Moot et al., 2000; Schittenhelm, 2001; Mathieu et al., 2014). The base temperature seems to be around 5 °C (Amaducci and Pritoni, 1998; Moot et al., 2000; Clapham et al., 2001) and the maximum temperature should be close to 35 °C (Mathieu et al., 2014; Langworthy et al., 2015). The default values used in the model for forage chicory are as given on Fig. 1. 2.1.3. Vernalising temperatures The chicory model uses the accumulation of vernalising degreesdays (VDD), or vernalisation effectives (Weir et al., 1984), to determine the extent of vernalisation. The temperatures ranging between 0 and 12 °C have been reported to induce vernalisation in chicory, but vernalisation is most effective at 4–6 °C (Wiebe, 1989, 1990; Gianquinto, 1997; Dielen et al., 2005). De-vernalisation also occurs in chicory (Wiebe, 1990; Gianquinto, 1997) and it is simulated in the model as a negative vernalisation effectiveness. This means that the effects of vernalisation are reversed if plants are exposed to sufficiently high temperatures soon after a period under vernalisation (less than 10 days in the model). Chicory shows a small reversal at medium temperatures (15–20 °C), but more effective temperatures to reverse vernalisation seem to be around 30–35 °C (Chouard, 1960; Wiebe, 1990; Gianquinto,

2.1.4.1. Leaf. This is described in the model using the SimpleLeaf approach. All leaves are simulated as a group, with no distinction of age or place in the canopy. The photosynthesis process is controlled by this organ, with a brief description given below. Leaf also handles water demand, but this is actually computed by the MicroClimate model (for more details see Brown et al. (2014) and Snow and Huth (2004)). In spite of several studies on N response, there are no data on the specific thresholds for nitrogen in chicory. The model uses a default of 2.5% for Nmin , as the lowest N concentration for leaves is around 1.5–3.0% (Clark et al., 1990a; Améziane et al., 1995; Bausenwein et al., 2001; Belesky et al., 2004). The critical N concentration defines the minimum content for optimal photosynthesis; based on general plant physiology and considering the results of several studies, values around 3.5–4.5% can be inferred (Clark et al., 1990b; McCoy et al., 1997; Neel et al., 2002; Alloush et al., 2003; Belesky et al., 2004; Lee et al., 2015); the model uses 4.0%. Finally, based on the available data, the value of Nmax is defined in the models at 5.5%. Data are also sparse for photosynthesis or radiation use efficiency

Fig. 1. Thermal time, or degrees-day (oCd), as function of air temperature; used for controlling phenological phases in forage chicory, where TB is the base temperature, TO is the optimum and TM is the maximum temperatures. 4

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general suggest the lower limit seems to be approximately 0.5–1.0%, and upper values range from 1.5 to 2.0% (Améziane et al., 1995; Bausenwein et al., 2001; Zagal et al., 2001; Bewley, 2002; Jurgoński et al., 2011). Based on available information, the values for Nmin and Ncrit are defined in the model at 0.7% and Nmax is set to 1.5%.

(RUE) for chicory, with most of the available data being from root cultivars. Reported maximum photosynthetic rates vary between 7 and 15 μmol (CO2) m−2 s-1 (Labreveux, 2002; Devacht et al., 2009) and estimates for RUE range from 1.5 up to 2.3 g DM MJ-1 (Monti et al., 2005; Devacht et al., 2009). The chicory model computes biomass accumulation, or fixation (BFix ), using the RUE approach (Monteith, 1977; Brown et al., 2014):

BFix = IRdn RUE min(fT fN fVDP ) fCO2 min(fW , fAer )

2.1.4.5. Root. This represents the fine roots, which are responsible for water and nutrient uptake. The root system can reach about 1.8–2.0 m down in the soil profile (Brown, 2004; Li and Kemp, 2005; Sapkota et al., 2012). Root growth is assumed to be at its maximum during vegetative and inductive phases (with a front velocity of 10 mm d−1), and slows down during reproductive development (to 10% of maximum front velocity). The uptake processes are affected by the root length density, which is computed from root biomass and the specific root length (SRL). Reported values for SRL range from 10 up to 100 m g−1 (Labreveux, 2002; Sanderson and Elwinger, 2004; Sapkota et al., 2012; Cranston et al., 2015; Liu et al., 2015); in the model a value of 40 m g−1 is used. All biomass in the roots is considered structural and with only a small difference between N thresholds (minimum and maximum N concentrations of 1.4% and 1.6%, respectively).

(1)

where IRdn is the intercepted total solar radiation, RUE is the efficiency value, set to 1.5 g DM MJ−1, and the remaining are factors limiting photosynthesis due to temperature ( fT ), N concentration ( fN ), vapour pressure deficit ( fVP ), CO2 concentration ( fCO2 ), water availability ( fW ) and soil aeration ( fAer ). Fig. X1 in the Appendix shows the variation pattern of these functions. Radiation interception is defined by Beer’s law, using the plant leaf area index (LAI) and the light interception coefficient, which is set to 0.6 in the model (Lee et al., 2015). The value of LAI for green leaves is defined based on the specific leaf area (SLA), which for forage chicory ranges from 0.015 to 0.045 m2 g−1 (Li et al., 1997a; Sanderson and Elwinger, 2000b; Labreveux, 2002; Alloush et al., 2003; Lee et al., 2015). Although age and environmental factors may affect it, the value for SLA is currently assumed to be constant in the model (defaults to 0.03 m2 g−1).

2.1.5. Biomass partitioning The biomass fixed by photosynthesis plus that reallocated from senesced tissue or remobilised from reserves is partitioned among organs by the organ arbitrator. This is done based on the relative demands of each organ (Brown et al., 2014). The demand is first controlled by phenology (e.g. the inflorescence only has demand during the reproductive phase) and then by the plant attempting to maintain given biomass ratios among the various organs. The approach of defining biomass ratios is new to PMF, which has been previously used primarily for annual crop species. The use of ratios was necessary to ensure regrowth after grazing or at the end of the reproductive phase (e.g. leaf demand is minimal during reproductive phase, except when the plant needs to recover from defoliation). This is in line with approaches such as Johnson and Thornley (1987), and it is a simplified implementation of biomass allocation plasticity (e.g. Levang-Brilz and Biondini, 2003; Poorter et al., 2012). A series of ratios are used sequentially to define biomass partitioning in the chicory model, starting with the shoot:root ratio. This is defined as a function of phenology, with a target set to 2.5 during the vegetative phase, increasing to a maximum of 4.0 during the stem elongation and reproductive phases. Published data show that the shoot:root ratio of chicory has also a strong cultivar effect (Li et al., 1997a; Zagal et al., 2001; Labreveux, 2002; Alloush et al., 2003; Belesky et al., 2004). Varieties bred for root harvesting can have shoot:root ratios as low as 1.0, whereas for leaf cultivars the values may be up to 5.0. Environmental factors, such as temperature, shading, or the availability water and nutrient can influence the shoot:root ratio, but currently the model does not explicitly account for these, with the exception of the effect of vernalisation. The chicory model changes the intensity in the shift of biomass allocation during reproductive development as a function of the extent of vernalisation. This is done because the chicory model describes a population, instead of individual plants, and as only a fraction of the plants are vernalised every year, around 50% (Clapham et al., 2001; Sanderson et al., 2003; Dielen et al., 2005), and this proportion seems to be linked to extent of vernalisation (Gianquinto, 1997; Dielen et al., 2005). Therefore, the model downregulates the targeted shoot:root ratio for the stem elongation and reproductive phases based on the VDD accumulated over the previous phases (Fig. 3). The actual demand of above-ground organs is computed based on the target shoot:root ratio (rSRt ) and the current ratio (rSRc ). This can be expressed, in terms of relative demand, as:

2.1.4.2. Stem. This represents all stems and branches of the chicory sward. Allocation of biomass to stems occurs only during reproductive developments (stem elongation and reproductive phases). The N thresholds are defined as 0.7% for Nmin and 2.3% for Nmax (Clark et al., 1990a, b; Jung et al., 1996; Li et al., 1997c); the value of Ncrit is assumed to be equal to the minimum value. The composition of stems has been shown to change with age (Clark et al., 1990b), with greater N content in young stems and much less in older ones. This probably reflects the accumulation of structural, woody, tissue and thus the chicory model assumes an increase in structural tissue in stems at the onset of the reproductive phase (with the structural fraction varying from 0.75 to 0.9). This approach captures the general trend observed in the field, but does not fully account for the effect of drastic or frequent removal of stems, which would result in only young stems with a greater proportion of non-structural biomass. 2.1.4.3. Inflorescence. This organ represents the flowers as well as the seeds of forage chicory. Few studies have been published with data that could be useful to parameterise these organs. The N concentration in the inflorescence of forage chicory seems to be considerably higher than in other organs (Clark et al., 1990b) and this can be related to flowering or seed development needs: mature seeds have a lower N content, about 2.5–3.0% (Jurgoński et al., 2011; Wenying and Juingui, 2012). Because chicory plants can have flower buds, open flowers, developing seeds and mature seeds at the same time, the N concentration of the inflorescence is assumed in the model to vary as a function of developmental stage, being the highest at the beginning of the reproductive phase and decreasing at the end of the phase (with Nmax peaking at 5.0%, Nmin as well as Ncrit being 90% of the maximum thresholds, and all decreasing to 3.0% at the end of the phase). 2.1.4.4. Taproot. This is the main storage organ in chicory and can supply N and sugars to boost plant growth in spring or after defoliation. This is represented in the model by the remobilisation of the nonstructural portion of the taproot’s biomass. The fraction of storage biomass has been set to 50% based on the average value from available data (Améziane et al., 1995; Ernst et al., 1995; Li et al., 1997a; Cranston et al., 2015). The rate that these reserves can be used is set to 0.05 d−1 and is down-regulated using limiting factors for temperature and water availability similar to those for photosynthesis, thus avoiding prompting growth during winter or in dry conditions. There are few data on N concentration available for taproots; reports for chicory in

DAG =

5

rSRt 2 rSRt 2 + rSRc

(2)

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Fig. 3. Schematic representation of the modelled variation in target shoot:root ratio of forage chicory for different phenological phases (a), and the relative effect of the extent of vernalisation on changes in the shoot:root ratio as function of vernalising degrees-days (VDD) accumulated over the vegetative and inductive phases (b).

using a mechanical mower and the biomass was removed from the field. This dataset is referred to as the ‘defoliation experiment’. The final datasets are from a comprehensive pair of experiments established at Lincoln University, Lincoln (43.63S 172.45E), and at Scott Farm, Hamilton (37.78S 175.32E), between 2015 and 2017. These were part of a large programme called Forages for Reduced Nitrogen Leaching (FRNL), thus the dataset are referred to hereinafter as ‘FRNLLincoln’ and ‘FRNL-Hamilton’. More details of the Lincoln experiment are provided in Martin et al. (2017). The soil at the Lincoln site is a freedraining Templeton Silt Loam (Udic Haplustept, USDA soil taxonomy) and the Horotiu Silt Loam (Typic Udivitrand, USDA soil taxonomy) at Scott Farm. There were six treatments (three replicates in Hamilton and four in Lincoln) consisting of different rates of N fertiliser (0, 50, 100, 200, 350, and 500 kg N ha−1 year−1, divided in approximately 10 applications over the year – actual amounts varied, as management was adjusted to crop growth). The plots were mowed mechanically at regular intervals and the biomass was removed from the field.

The allocation of biomass in each specific organ is also controlled by target ratios, which are defined as function of phenology as well. The relative demand of each organ is then defined by balancing the target ratio and the respective current values, in the same way as the above description for shoot-root partitioning. These ratios were defined for: leaf versus non-leaf organs above ground (which can reach a value of 1.0 during reproductive development), inflorescence to stems (up to 0.1), and taproots to roots (also set to a maximum of 1.0). The taproot:root ratio is defined as a function of below-ground biomass and is not influenced by phenology. The variation pattern for these ratios are shown in Fig. X2 of the Appendix, their thresholds were inferred from published data (Clark et al., 1990b; Jung et al., 1996; Li et al., 1997a, 1998; Zagal et al., 2001; Labreveux, 2002; Belesky et al., 2004).

2.2. Model testing 2.2.1. Experimental datasets Data from four experiments were available in sufficient detail to enable comparison against simulation results. For these, environmental characteristics and recorded management were used to set up APSIM simulations describing plots of pure stands of forage chicory. A brief introduction to each experiment and dataset is provided here; more details can be found in the references given alongside. All data are from New Zealand because they were the experiments for which sufficient details were available to set up the simulation completely and thus allow fair comparison between model results and measured data. Unfortunately no experiment included measurements of plant phenology, so these are not tested here. The first experiment was performed at the Iversen field of Lincoln University, Lincoln (43.65S 172.46E), between 1996 and 2002 (Brown, 2004; Brown et al., 2005). This is called hereinafter the ‘Iversen experiment’. The soil is an imperfectly drained Wakanui Silt Loam (Udic Ustochrept, USDA soil taxonomy), which was fertilised during the experiment, but not with N. The two treatments consisted of dryland and irrigated plots (three replicates); the irrigation was triggered by soil water deficit and was applied using travelling mini-boom irrigator. The plots were grazed by sheep of mixed ages several times during each year. Herbage biomass was measured just before each grazing and at 7to 10-day intervals within regrowth cycles. Soil moisture was monitored throughout the experiment, down to a depth of 2.3 m. Another dataset comes from an experiment performed at DairyNZ’s Scott Farm in Hamilton (37.78S 175.32E) between 2010 and 2012 (Lee et al., 2015). The soil is a well-drained Horotiu Silt Loam (Typic Udivitrand, USDA soil taxonomy) and it received regular applications of K and N fertilisers. The treatments consisted of four defoliation intervals (five replicates), defined by pre-harvest sward height (15, 25, 35, and 55 cm), and two residual heights (4 or 7 cm). Defoliation was done

2.2.2. Modelling setup The model development and all the simulations were made in APSIM Next Generation (www.apsim.info). Management was controlled using APSIM’s Manager scripts (Moore et al., 2014) to represent the relevant actions as provided in the respective publications or through personal communication with their authors. Soil data were obtained from measurements available for the respective soil (Watt and Burgham, 1993; Close et al., 2003; Brown, 2004) and complemented with data from the NZ National Soil Database (Landcare Research – viewernsdr.landcareresearch.co.nz). Remaining parameters were obtained using pedo-transfer functions (Vogeler et al., 2011; Cichota et al., 2013). The procedure to set up soil parameters in APSIM is described elsewhere (e.g. Cichota et al., 2012) and the final parameters are given in the Appendix (Table X1). Weather data were obtained from the nearby Broadfield met station (#17603) for Lincoln and the Ruakura met station (#26117) for Scott Farm (NIWA – cliflo.niwa.co.nz). A summary of long-term averages for the two stations are given in Table X2 of the Appendix. 2.2.3. Statistical analysis Model performance was assessed using a combination of common statistical measures (Moriasi et al., 2007; Bennett et al., 2013). These included the coefficient of determination (R2), the Nash-Sutcliffe efficiency score (NSE), and the root mean squared error (RMSE). They provide indices for the goodness of agreement between modelled and observed data, as well as the accuracy of model predictions. The value of R2 ranges between 0.0 and 1.0, with higher values indicating better agreement. The range for NSE values goes from -∞ to +1.0; with a positive value indicating that the model has more predictive power 6

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Fig. 4. Measured (dots) and APSIM-simulated (lines) above-ground biomass of forage chicory under two irrigation regimes in the Iversen experiment (Lincoln, New Zealand).

relatively low R2 value, 0.40, but the NSE was 0.37. The agreement was stronger for FRNL-Hamilton and weaker for FRNL-Lincoln (R2 of 0.54 and 0.38, and NSE of 0.49 and 0.30, respectively). Standing biomass was not measured in the defoliation experiment. For all data combined, the R2 was 0.45 and the NSE was 0.40. The RMSE values for each experiment ranged between 50 and 90 g m−2, or about 70% of the standard deviation of observations. The observed yields of forage chicory also showed large variability among replicates; for instance, the coefficient of variation for the Iversen experiment was approximately 40%. This reflects the difficulty in measuring biomass harvested with great precision, as well as the variability in space and over time (e.g. White et al., 2008). This variations can generally be minimised by using the cumulative values over a growing season, as random measuring errors should be diluted evenly over all samples. Considering the individual values, the chicory model performed reasonably well, but the measures for goodness of agreement were higher when comparing the harvests as cumulative over the growing season (Table 1). The agreement was also closer for the two experiments at Scott farm (defoliation and FRNL-Hamilton experiments), where both R2 and NSE were approximately 0.60, whereas for the experiments at Lincoln these measures were around 0.25. Considerably higher values for the goodness of agreement measures were obtained when comparing the cumulative harvested amounts, albeit more variable. For instance, the R2 for all data combined went from 0.41 to 0.67 when considering the cumulative harvested amount, and a similar variation was also seen for the NSE (Table 1). The lower values for the agreement measures for non-cumulative harvests suggest that the model can be improved in the future; for instance, growth over spring was found to be often underestimated and yields tend to be overestimated for older stands. This is probably because the model does not currently capture the effects of competition from weeds and/or

Table 1 Goodness of agreement measures between APSIM-simulated and observed amounts of forage chicory harvested in four field experiments. Comparisons were made for individual harvests as well as the values accumulated over a growing season (July to June). R2 is the coefficient of determination, NSE is the Nash-Sutcliffe efficiency score, and RMSE (g m−2) is the root mean squared error. Individual harvest

Season cumulative

Experiment

R

2

NSE

RMSE

R2

NSE

RMSE

Defoliation Iversen FRNL-Hamilton FRNL-Lincoln All data

0.60 0.24 0.62 0.27 0.41

0.61 0.24 0.53 0.20 0.35

58.3 100.8 63.7 65.7 73.2

0.89 0.77 0.83 0.64 0.67

0.83 0.74 0.66 0.26 0.53

128.5 184.0 167.9 280.1 220.9

than simply using the mean of the observed values. A close agreement between model and observed data also has small values for RMSE, preferably smaller than 70% of the observed standard deviation (Moriasi et al., 2007). The statistical measures were calculated using the values for all the replicates of the field measurements, not the average by treatment.

3. Results and discussions The accumulation of biomass over time simulated by the chicory model in APSIM was in reasonably good agreement with the available data. The time series for the Iversen experiment, which had the longest duration, encompassing an establishment season and five full growing seasons, is shown in Fig. 4. The data show considerable dispersion and model failed to describe some of the higher values. This resulted in a 7

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Fig. 5. Observed (dots) and simulated (lines) yields of forage chicory in two experiments at Scott farm (Hamilton, New Zealand) under different defoliation regimes (a) and various annual fertiliser rates (b). Data accumulated over each growing season (July to June); more details about the treatments in the text.

standard deviation (0.08 g g−1). Given the weak description of N content in the biomass, the amounts of N taken from the field in the harvested material were not simulated well in all experiments (Fig. 7). The R2 was 0.34 and NSE of 0.08 for individual dates and all data combined, but these increased to 0.61 and 0.56, respectively, when comparing the data cumulatively over each growing season. These values would suggest a reasonable model performance, but comparisons per experiment or treatment showed large deviation. Improving the description of the variations in N concentration in the different organs over time is still required before the model can be used with confidence to simulate N balance in forage chicory swards. The soil water content simulated by APSIM using the chicory model was able to describe the measured values from the Iversen experiment well (Fig. 8). The total amount of water over the measured profile (down to 2 m), was simulated with an RMSE of 39 mm, which is approximately 40% of the measured standard deviation; R2 and NSE were 0.83 and 0.81, respectively. The measures for goodness of agreement varied considerably for individual layers, with R2 between 0.40 and 0.85 for the layers in the top 1.5 m, and NSE varying between 0.25 and 0.80. For the deeper layers the measures decreased sharply, with R2 near zero and negative values for NSE. This was expected, as only small temporal variations in soil water content were recorded at these layers, with no observable water uptake below 1.90 m (Brown et al., 2005). The forage chicory model is available for use within the APSIM modelling framework. The tests presented here show that the model had an overall average performance when simulating monoculture chicory swards. The model should be used with care, as refinements are needed to improve how it describes N cycling in the plant. Future improvements in the model will demand experiments that can provide more detailed information regarding the processes that control the partition and mobilisation of N amongst the various chicory organs. More information is also needed on how biomass allocation changes across seasons, especially the dynamics of the build-up and use of plant reserves (storage biomass fraction). The use of the chicory model in mixed swards is possible, as is the case with other APSIM plant models, but tests under these conditions have not yet been conducted and are part of ongoing work.

fungal diseases, which can reduce the population of forage chicory (Moloney and Milne, 1993; Li and Kemp, 2005), and are linked to its low persistence. RMSE values ranged between 60 and 100 g m−2 for individual harvests (Table 1), and were similar or lower than the standard deviation of the observations. When considering the season cumulative values, the RMSE was larger (128-280 g m−2), but represented less than 50% of the observed standard deviation. This degree of accuracy is in line with the performance of other forage models (e.g. White et al., 2008; Li et al., 2011; Ojeda et al., 2016). The modelled results showed small differences in harvested amounts when various defoliation regimes were simulated, in general agreement with the results from the defoliation experiment (Fig. 5), although total yields were reported to be significantly affected by defoliation (Lee et al., 2015). The treatments imposed in that experiment did not appear to be contrasting enough to produce large effects in the harvested amounts (e.g. Jung et al., 1996; Li et al., 1997c). The application of irrigation resulted in an increase of the simulated yield by 23% compared with no irrigation, over the six growing seasons of the Iversen experiment. This compared well with the 17% recorded in the observed data, which is a relatively small effect considering that annual rainfall is typically well below potential evapotranspiration in Lincoln (Table X2, in the Appendix). The high water storage capacity of the soil used in that experiment, combined with the deep root system of chicory, contributed to the low response to irrigation (Brown et al., 2005). Forage chicory also showed high sensitivity to application of N fertiliser, which was investigated in the two FRNL experiments (see also Belesky et al., 2000; Martin et al., 2017). The observed response of chicory yield to N fertiliser rates was simulated reasonably well, R2 about 0.6, by the model (Fig. 5), with some overestimation for the larger N rates and on the older stands. This mismatch is likely due to a combination of factors, but primarily because the model does not yet consider the effects of disease and the decline of population over time. The temporal variations in N concentration in above-ground tissues were captured by the model (Fig. 6), but there were considerable underestimations in the low fertiliser treatments. This may reflect an underestimation of mineralisation of soil organic matter, for which there were no data, but it is more likely to be an indication that the model needs a more sophisticated description of the interaction between leaf area and N supply. The observed data also show large variability, both between replicates in each measuring date and between consecutive observations. This variability contributed to the poorer values for the goodness of agreement measures compared with those for biomass. The R2 was only 0.20-0.25 and NSE had negative values (-0.20-0.40), with similar values for individual experiments as well as for all data combined. The RSME for N concentration was approximately 0.09 g g−1, which is slightly greater than the observed

4. Conclusions Based on a review of the botany of chicory and its use as forage, a model for simulating the phenology and biomass accumulation of forage chicory was developed for the APSIM framework using the PMF. The model was tested against four field experiments which included a variety of treatments, including irrigation, fertiliser rates, and defoliation regimes. The model performed well compared with measured biomass data, being able to capture the effects of the various treatments 8

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Fig. 6. Observed (dots) and APSIM-simulated (lines) variation in N concentration of above-ground biomass of forage chicory over two growing seasons in four of the treatments of FRNL-Lincoln experiment; treatments were no fertiliser (a), and annual fertiliser rates of 100 (b), 200 (c) and 500 kg N ha−1 (d).

Fig. 7. Observed (dots) and APSIM-simulated (lines) seasonal cumulative amounts of N harvested from two experiments with forage chicory, in Hamilton (a) and Lincoln (b), New Zealand, under various annual N fertiliser amounts (more details about the treatments in the text).

as well as different soils and climates. The agreement measures were poorer for N concentration; thus the model should be used with care. More data are needed to fully validate biomass and N partitioning between the various organs; nevertheless, the general performance of the model should be acceptable for farm systems analyses. The model is now part of the APSIM modelling framework and is available for use in farming systems simulations.

New Zealand Ministry of Business, Innovation and Employment (DNZ1301) and AgResearch Limited. The programme is a partnership between DairyNZ, AgResearch, Plant & Food Research, Lincoln University, the Foundation for Arable Research, and Landcare Research.

Acknowledgments

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.fcr.2019.107633.

Appendix A. Supplementary data

The research was completed as part of the Forages for Reduced Nitrate Leaching (FRNL) programme, with principal funding from the 9

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Fig. 8. Measured (dots) and APSIM-simulated (lines) average soil water content over the 2.3-m profile of a Wakanui soil under two irrigation regimes in the Iversen experiment (Lincoln, New Zealand).

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