Simulation using the STICS model of C&N dynamics in alfalfa from sowing to crop destruction

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Highlights

  • We developed a generic model able to simulate biomass allocation, N partitioning and reserve remobilization within plants.

  • Harvested biomass and forage quality was accurately predicted for establishment and regrowth periods of alfalfa.

  • The model adequately simulated the effect of cutting frequency on biomass production and quality.

  • The model reproduced efficiently the soil mineral N content during cropping and after alfalfa destruction.

Abstract

We adapted the STICS agro-environmental model to simulate the effects of cultivation practices on the biomass production and nitrogen accumulation of perennial crops undergoing regular defoliation, using alfalfa as an example. A unique set of parameters was used to simulate both establishment and regrowth phases over several years, with the assumption that crop growth is driven by interaction between crop development stage and abiotic stresses. The model accurately simulated the total biomass (stems + leaves + crown + taproot + roots) and aboveground biomass of the crop, with model efficiencies of 0.75 and 0.70, respectively, and relative root mean squared errors (rRMSE) of 42% and 36%, respectively. The evaluation results were also satisfactory with respect to total nitrogen content and the aboveground biomass nitrogen content, with model efficiencies of 0.90 and 0.60, respectively, and rRMSE values of 29% and 31%, respectively. The model thus enabled simulations of both the establishment and regrowth of alfalfa and accurately reproduced its seasonal patterns of growth, even though it tended to underestimate spring biomass production. It also produced accurate simulations of the water and nitrate contents of the soil during cropping and after crop destruction. It could therefore be a useful tool regarding the multi-criteria assessment of cropping systems based on alfalfa with respect to their sustainability.

Graphical abstract

Dynamic simulation of biomass partitioning during the initial growth of spring seedlings and subsequent regrowth. Blue line = simulated total biomass (t DM ha-1); Green line = simulated aboveground biomass (t DM ha-1); Brown line = simulated structural stem biomass (t DM ha-1); Grey line = simulated green leaf structural biomass (t DM ha−1); Blue circle = observed total biomass (t DM ha−1); Green square = observed aboveground biomass (t DM ha−1); Brown triangle = observed stem biomass (t DM ha−1); Grey cross = observed green leaf biomass (t DM ha−1).

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Introduction

Cropping systems based on alfalfa are often promoted not only for the quantity and quality of their forage production but also for the other ecosystem services they can supply, such as sustaining nitrogen levels within the system because of ability of alfalfa to fix atmospheric N2 (Louarn et al., 2015; Vertes et al., 2015). To take account of the positive effects of alfalfa on soil N mineralisation, it is necessary to consider the quality and quantity of plant residues that are returned to the soil during the life of the crop and after its destruction. With respect to forage production, both quality and quantity are dependent on the timing and frequency of harvests throughout the year (Lloveras et al., 1998; Martiniello et al., 1997) and on physiological requirements in terms of plant development and accumulating reserves (Justes et al., 2002). In this context, agro-environmental models adapted to deal with alfalfa during its whole growth cycle could be extremely useful. Indeed, such agro-environmental models would enable simulation of the effects of management practices on both crop production and quality as well as on long term environmental impacts in terms of crop rotations. The purpose of our study was therefore to adapt the STICS model to this context. STICS has been positively evaluated in 15 crops across a broad range of agro-climatic conditions at the annual scale (Coucheney et al., 2015) and to predicting N dynamics in rotations of annual crops in Europe (Yin et al., 2017).

Several challenges are involved when simulating alfalfa biomass production as it is influenced by both the environment and cultivation practices throughout the year. Firstly, the growth and development of alfalfa crops is strongly dependent on environmental signals across the seasons, notably the photoperiod and temperature. Kalu and Fick (1981) showed that crops growing in the spring and summer reached the flowering stage after around 42 days, but this stage was never reached by crops cultivated during the autumn, even after 60 days of growth. A marked variability in photoperiod sensitivity was also demonstrated in different alfalfa cultivars, and a short photoperiod increased the time required to reach the flowering stage (Major et al., 1991). This effect of photoperiod on plant growth and development was only observed during autumn in alfalfa (Moot et al., 2003). Clerget et al. (2004) suggested that decreasing photoperiods may act as a direct signal to explain such a response. In addition, although the radiation use efficiency of alfalfa with respect to total biomass production (i.e. crown + taproot + aboveground organs) was shown to remain stable throughout the year, the apparent radiation use efficiency for above ground biomass decreased in autumn (Durand et al., 1989; Khaiti and Lemaire, 1992), indicating a change of biomass allocation within the crop. In view of these findings, we have therefore advanced hypothesis “H1”, which states that seasonal changes occur regarding the partitioning of biomass to roots and that photoperiod acts as a driving signal for it. The current standard version of STICS (v9) is able to simulate the effects of photoperiod on crop development via the calculation of a photo-thermal index (PTI) of development (Brisson et al., 2009), but it does not account for the direct effects of photoperiod on growth and dry matter partitioning. The model has therefore been adapted so that PTI could affect dry matter partitioning between perennial and non-perennial organs (and thus indirectly root growth and apparent aboveground RUE).

Secondly, the initial establishment phase of alfalfa after sowing differs markedly from any regrowth after a cut (Teixeira et al., 2011; Thiébeau et al., 2011). The dynamics of leaf area index (LAI) and aboveground biomass accumulation are less rapid during the early stages after sowing as a result of a higher relative investment in the roots (Louarn and Faverjon, 2018). Moreover, the phenological development of alfalfa differs, with a delayed flowering time for seedlings when compared to regrowing crops, that can partly be explained by poorer N nutrition (Teixeira et al., 2011; Faverjon et al., 2018). The biomass production of alfalfa is also sensitive to the cutting rate. Indeed, the amount of N remobilised after cutting or grazing depends on the N reserves stored in the taproot and crown, and to a lesser extent in the lateral roots (Kim et al., 1993; Ourry et al., 1994). The dynamics of regrowth depend on accumulated N reserves (Dhont et al., 2003; Justes et al., 2002; Ourry et al., 1994) which are directly impacted by the cutting rate, and on the residual leaf area index after a cut (Meuriot et al., 2004). It is therefore essential to take account of the nitrogen fluxes between perennial and non-perennial organs when trying to simulate alfalfa growth mechanistically, as suggested by Teixeira et al. (2008). Avice et al. (1996) demonstrated in their study that the biomass reserves remobilised from source organs during regrowth after a cut were mainly used for respiration and thus for energy production, with only 5% of remobilised C recovered contributing to the newly formed organs. Moreover, Bourgeois et al. (1990) conducted a sensitivity analysis of the ALF2LP model and showed that biomass production was little sensitive to the non-structural carbohydrate reserves available before regrowth. We have therefore advanced hypothesis “H2”, which states that the differences in crop growth and development between seedlings and regrowing crops were not truly ontogenic i.e. resulting from different growing dynamics according to developmental stages (sowing or regrwowing crops), but were instead due to the abiotic stresses felt by the crops in function of their growth or development (rooting depth, nodule development) and of their growth history (reserve content, residual leaf area). Indeed, the N remobilisation from perennial organs during regrowth and the presence of a developed root system after cutting enable the plants to partly or totally avoid N stress, leading to more rapid growth and development of the crop. The STICS model has recently been updated for the simulation of perennial crops and now enables the simulation of biomass production and N accumulation as well as their partitioning between perennial, non-perennial organs and roots (Strullu et al., 2014, 2015). Therefore, using these new equations initially developed and evaluated for Miscanthus × giganteus should allow us to capture the growth of seedling and regrowing alfalfa crops using a unique set of parameter values.

Finally, when it comes to simulating regularly harvested forage crops, the STICS v9 is not able to cope with multiple regrowth cycles during the year and to take account of possible interactions between shoot growth and roots and the residual leaf area index remaining after each cut (e.g. Miscanthus × giganteus is cut once a year and remobilises biomass and N only in spring). We have therefore advanced hypothesis “H3”, which states that the effects of cutting rate on crop regrowth after a cut is mediated by residual leaf area index and the amount N reserves in the perennial organs at the time of the cut (Ta et al., 1990; Avice et al., 1996; Volenec et al., 1996; Dhont et al., 2003; Meuriot et al., 2004; Teixeira, 2006).

Different models have been developed to simulate alfalfa. Some of them dealt with biomass (Moot et al., 2015; Teixeira et al., 2009; Bourgeois et al., 1990; Denison and Loomis, 1989) or nitrogen partitioning (Lemaire et al., 1992a; Faverjon et al., 2018) within the different organs of the plant. The ALSIM 1 model (Level 2) developed by Fick (1981) also dealt with the remobilisation of carbohydrates from the taproot during alfalfa regrowth. However, all these models are crop-specific and do not enable the simulation of crop rotations. The models that can simulate rotations, such as CropSyst (Confalonieri and Bechini, 2004) and CROPGRO (Malik et al., 2018), have been evaluated for their ability to simulate the biomass production of alfalfa. However, to our knowledge, none of those models has been evaluated for its ability to simulate biomass production, biomass and N partitioning within the crop, and soil water and N content in seedlings and regrowing crops subjected to contrasted agricultural practices. Moreover, none of them has been evaluated for its ability to simulate N mineralisation after the destruction of alfalfa. From an agronomic point of view, the accurate modelling of C and N inputs to the soil during the production phase of perennial crops and after their destruction is also essential in order to simulate changes to soil organic carbon and nitrogen levels over time. The insertion of perennial crops in cropping systems could enhance C sequestration in soils (Autret et al., 2016; Ferchaud et al., 2016) and improve N mineralisation rates after their destruction (Justes et al., 2001; Yin et al., 2020). Consideration of these aspects is therefore essential in order to simulate complex crop rotation systems that include perennials.

Our aims were to investigate the ability of this updated model to simulate alfalfa in terms of: i) the effect of the interaction between crop development stage and abiotic stresses felt by the crop (seedling versus regrowing crops) on biomass and N partitioning within the crop, ii) the effects of cutting rates on regrowth dynamics, yield and quality, iii) the effect of growing season (spring, summer or autumn) on biomass and N accumulation as well as on biomass and nitrogen partitioning within the plant and iv) the impact of alfalfa on the water and mineral N content of the soil, as well as on N mineralisation following crop destruction.

Section snippets

Description of the model

The STICS model was developed to simulate the effects of climate, soil and management practices on plant growth, development and production (quantity and quality) and environmental impacts (Brisson et al., 1998). It can be applied to a single crop that is harvested once or several times, two intercropped or several successive crop cycles. The model STICS v9 was described by Brisson et al. (2009) and has recently been evaluated over a large data set for 15 different crops and different

Model calibration

The principal parameter values used to simulate both establishment and regrowth cycles are summarised (Table 2, Table 3) and values used in STICS v9 are summarised in ESM2-T1 and ESM2-T2. The model accurately captured changes to the plant leaf area index, total biomass, aboveground and perennial organ biomass, total N content, aboveground and perennial organ N content and NNI observed in the calibration dataset with R² and EF higher than 0.55 and rRMSE lower than 40% (Table 4). The IPQ was

A plastic source-sink formalism of C and N allocation to perennial reserves

During this study, we developed a mechanistic model to simulate biomass and N partitioning in plants in a realistic manner. Different strategies have been used by modellers to simulate the evolution of biomass partitioning in alfalfa. For example, Fick (1981) used variable biomass partitioning coefficients between stems, leaves and taproot as a function of photoperiod. Malik et al. (2018) did not take account of the effect of photoperiod on biomass partitioning and chose to use variable biomass

Conclusions

During this study, we assessed a generic model capable of simulating biomass and N partitioning within an alfalfa crop in a realistic manner and the mobilisation of reserves as a function of management practices and crop development stage. The model performed well with respect to both biomass production and quality. However, some improvements are still required regarding the simulation of biomass production in spring, the simulation of the difference in latency period as a function of cutting

Acknowledgments

The authors would like to thank all the people involved in acquiring the experimental data used for this study at the INRA sites in Lusignan, Grignon, Fagnières, Reims, Estrées-Mons and Laon. The authors thank all physiologists and modellers whose work and publications enabled the modelling of plant processes. We also thank anonymous reviewers for their fruitful comments This research formed part of the VariLuz project which received a grant from the French Ministry of Agriculture (CASDAR

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