Simulation using the STICS model of C&N dynamics in alfalfa from sowing to crop destruction
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).
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
References (61)
- et al.
Alternative arable cropping systems: a key to increase soil organic carbon storage? Results from a 16 year field experiment
Agric. Ecosyst. Environ.
(2016) - et al.
Evaluation of the soil crop model STICS over 8 years against the “on farm” database of Bruyères catchment
Eur. J. Agron.
(2008) - et al.
Evaluation of an Alfalfa growth simulation model under Québec conditions
Agric. Syst.
(1990) - et al.
Does panicle initiation in tropical sorghum depend on day-to-day change in photoperiod?
Field Crops Res.
(2004) - et al.
A preliminary evaluation of the simulation model CropSyst for alfalfa
Eur. J. Agron.
(2004) - et al.
Long-term nitrogen dynamics in various catch crop scenarios: test and simulations with STICS model in a temperate climate
Agric. Ecosyst. Environ.
(2012) - et al.
Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: evaluation over a wide range of agro-environmental conditions in France
Environ. Model. Softw.
(2015) - et al.
Analysis and classification of data sets for calibration and validation of agro-ecosystem models
Environ. Model. Softw.
(2015) - et al.
Dynamics of shoot and root growth of lucerne after seeding and after cutting
Eur. J. Agron.
(1992) - et al.
Statistical and graphical methods for evaluating solute transport models: overview and application
J. Contam. Hydrol.
(1991)
Effect of phenological stages on dry matter and quality components in lucerne
Eur. J. Agron.
River flow forecasting through conceptual models part I—a discussion of principles
J. Hydrol.
N2O emissions of low input cropping systems as affected by legume and cover crops use
Agric. Ecosyst. Environ.
Cover crops mitigate nitrate leaching in cropping systems including grain legumes: field evidence and model simulations
Agric. Ecosyst. Environ.
Growth and phenological development patterns differ between seedling and regrowth lucerne crops (Medicago sativa L.)
Eur. J. Agron.
Defoliation frequency and season affected radiation use efficiency and dry matter partitioning to roots of lucerne (Medicago sativa L.) crops
Eur. J. Agron.
Radiation use efficiency and shoot:root dry matter partitioning in seedling growths and regrowth crops of lucerne (Medicago sativa L.) after spring and autumn sowings
Eur. J. Agron.
Performance of process-based models for simulation of grain N in crop rotations across Europe
Agric. Syst.
How variable are non-linear developmental responses to temperature in two perennial forage species?
Agric. For. Meteorol.
Nitrogen and carbon flows estimated by 15N and 13C pulse-chase labeling during regrowth of alfalfa
Plant Physiol.
Validation of biophysical models: issues and methodologies
STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn
Agronomie
Conceptual Basis, Formalisations and Parameterization of the STICS Plant Model
Contribution à la modélisation de la production de la luzerne: mise en œuvre et validation d’un modèle de simulation dans le cadre de l’activité déshydratation en Champagne-Ardenne
An Integrative Physiological Model of Alfalfa Growth and Development. Publ. No. 1926. Division Agricultural Natural Research
Simulation of Assimilation, Respiration and Transpiration of Crops
Alfalfa root nitrogen reserves and regrowth potential in response to fall harvests
Crop Sci.
Analyse de la conversion de l’énergie solaire en matière sèche par un peuplement de luzerne (Medicago sativa L.) soumis à un déficit hydrique
Agronomie
A generic individual-based model can predict yield, nitrogen content, and species abundance in experimental grassland communities
J. Exp. Bot.
Changes in soil carbon stocks under perennial and annual bioenergy crops
GCB Bioenergy
Cited by (15)
Calibrating the STICS soil-crop model to explore the impact of agroforestry parklands on millet growth
2024, Field Crops ResearchAdding a diversity of legumes to a crop decision-support system: Maintaining satisfactory accuracy while keeping the model simple
2023, European Journal of AgronomySimulation of alfalfa yield with AquaCrop
2023, Agricultural Water ManagementDevelopment of a lucerne model in APSIM next generation: 3 Biomass accumulation and partitioning for different fall dormancy ratings
2023, European Journal of AgronomyPrediction of rainfed corn evapotranspiration and soil moisture using the STICS crop model in eastern Canada
2022, Field Crops ResearchCitation Excerpt :The crop canopy is described in terms of shoot dry biomass (carbon and nitrogen), LAI, and the biomass (number and mass) of the harvested crop organs. The STICS version used in this study was a research version under development (STICS v2532) derived from the version 9 of the model and initially developed to improve simulation of perennial plants (Strullu et al., 2014, 2020). It also included better simulation of root distribution in the soil profile for all plants including annual plants such as corn.
Development of a lucerne model in APSIM next generation: 2 canopy expansion and light interception of genotypes with different fall dormancy ratings
2022, European Journal of AgronomyCitation Excerpt :For example, the modified APSIM lucerne model used a simple function of LAI expansion in response to Tt accumulation (Moot et al., 2015). Similarly, Strullu et al. (2020) used a constant LAI expansion rate in the STICS lucerne model. A second approach is to predict leaf area from simulated leaf dry weight using leaf biomass and specific leaf area (SLA).