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Process-based models (PBM) are important tools for understanding the benefits of Integrated Crop-Livestock Systems (ICLS), such as increasing land productivity and improving environmental conditions. PBM can provide insights into the contribution of agricultural production to climate change and help identify potential greenhouse gas (GHG) mitigation and carbon sequestration options. Rehabilitation of degraded lands is a key strategy for achieving food security goals and can reduce the need for new agricultural land.
- mixed-pasture
- soybean
- sandy soil
1. Introduction
Integrated Crop-Livestock Systems (ICLS) are defined by the diversification, rotation, consortium, and succession of agriculture and livestock inside the same area, allowing benefits between these two activities and the economic use of soil over the year [1]. The adoption of ICLS comprises 10–15 million ha (5% of total) of the area under agriculture and livestock in Brazil [2], which represents a significant opportunity to improve the sustainability of agricultural systems [3], especially in degraded areas and over sandy soils.
With the integration of soybean and mixed-pasture, ICLS impacts plant growth as the nitrogen (N) supplied by legumes (for instance, soybean) is absorbed more efficiently than that applied through mineral fertilization, stimulating plant growth and biomass quality, or both, through increased N uptake [4]. Lower dry matter production in grass monoculture pastures tends to decrease soil moisture, organic carbon (C), and total N compared to ICLS [5].
In this context, the applications of process-based ecosystem models are fundamental to evaluate and improve the level of understanding of these systems and predict processes such as plant growth, C dynamics, and carbon dioxide (CO2) flux. The advantages of using process-based ecosystem models are the possibility of extrapolation of the simulations from an experimental site to different sites and climate conditions [6], making it possible to have a consistent evaluation of other production systems [7][8][9][10].
DayCent is an ecosystem process-based model, and when properly validated by field observations is a powerful tool to investigate the effects of management practices on greenhouse gases (GHG) emissions or soil organic carbon (SOC) changes in different ecosystems, soil types, and climates [11]. Frolking et al. [12] demonstrated the ability of the DayCent model [13][14] to simulate soil water content, mineral N levels, nitrous oxide (N2O), and CO2 emissions for various systems, including a native shortgrass steppe in Colorado, a ryegrass pasture in Scotland, and perennially cropped soils in Germany.
In tropical and subtropical agriculture, simulations aiming to test the performance of DayCent in predicting SOC, biomass production, crop yield, and N2O fluxes were also performed [15]. They evaluated the model for agroecosystems under succession/rotational managements with crops such as wheat, soybean, sorghum, oat, and maize considering different management and climate scenarios in southern Brazil, and the predictive accuracy varied from poor (plant growth) to adequate (SOC and N2O fluxes) [15]. Damian et al. [16] evaluated the effects of converting poorly managed pastures to more intensive and diversified pasture management systems in Brazil, such as ICLS, on long-term soil C stocks and microbial biomass C increased with the conversion of pastures to ICLS using the DayCent model.
However, due to the diversity and complexity of ICLS, model predictions need to be adjusted and validated considering the region and system adopted. For instance, complex agroecosystem designs such as ICLS generally present the different combinations of plant species, directly impacting the soil C fluxes [17]. Additionally, after implementation, ICLS often have higher stocking rates and differences in animal trampling and excretion patterns [18] and the additional inputs with fertilization and biological N fixation [17][19]. Furthermore, the CO2 assimilated by the photosynthetic process is the basis of crop production [20]. Therefore, it is necessary to consider that the ICLS is the system that fixes the most CO2, and emits the least GHG, throughout the year. This information is essential for the current global scenario, mainly due to carbon trading, an environmental management tool for controlling carbon emissions [21].
2. Equilibrium
The replacement of natural ecosystems by agroecosystems with crops usually provides the decline in soil C content due to the reduction in the microbial C and the increase of SOM decomposition [22]. With the removal of the native vegetation and the beginning of the extensive pasture, the C stocks dropped in the soil. This may have been caused by the soil preparation (disturbing), breaking the aggregates offering protection to SOM and stimulating the biological activity [23].
In tropical ecosystems, the losses of C observed after the land clearing and cultivation of soils are more accelerated than in temperate regions [24]. Thus, the decrease of nutrients in an extensive pasture in native vegetation is due to soil revolving, even if minimal, since there is a negative effect on SOM [25][26][27]. Climatic conditions, especially temperature, also affect the decomposition rate of plant residues and the stabilization of SOM [28]. The occurrence of higher temperatures tends to accelerate the decomposition rate of SOM [29]. One alternative to increase the total C stock in the agriculture systems is by applying sustainable management practices and adopting crop systems that increase primary production and reduce C removal and soil degradation. Therefore, improved management practices affect the factors that regulate the synthesis and decomposition of SOM, contributing to the sustainable productivity of the ecosystem [30].
3. Calibrated Model
The DayCent model adequately simulated aboveground biomass, mainly for the beginning of the soybean crop and the mixed-pasture growth phases, periods that had no direct grazing effect on the simulations; even though it was an area with intensive pasture management, there were periods just after the mixed-pasture implementation where the animals were not present in some paddocks. All nine paddocks had the same altered parameters, and the behavior throughout the experiment was similar. Thus, minor model failures in predicting aboveground biomass did not influence each paddock’s overall behavior. Due to the study area being a commercial farm, minor problems with pests, diseases, and weeds may also have influenced the biomass production to some extent, all of which are not currently captured by the DayCent model unless forcing the model to reproduce the reductions of potential plant growth.
Del Grosso et al. [31] reported that most of the errors in DayCent model outputs were associated with imperfections in model algorithms and parameters instead of uncertainty in model drivers. Therefore, efforts to improve the model should compare model outputs using numerous observations for various C and N components from field experiments to identify weaknesses and rectify model shortcomings [32].
In ICLS, there is a differentiated contribution of plant residues regarding conventional grain production systems, both in the surface and soil subsurface [33]. In intensive ICLS, the often succession of crop and pasture planting contributes to the root growth of both plants and potentially increases the SOM in the deeper soil layers [34]. SOM can be investigated by detailing its behavior over time in the future. Not only the inclusion of crops in pasture areas can contribute to improving the system, but also planting pastures in crop areas can increase crop yield by improving edaphic properties, the presence of straw and pasture roots, increasing C levels and significantly ameliorating the conditions of aeration and water infiltration capacity in the soil [35]. In future, people need to investigate the GHG mitigation which ICLS can potentially offer, which farmers can use to understand their carbon footprint, encouraging management strategies to improve agricultural sustainability [36].
4. Validated Model
In the validation stage, the DayCent model performed well in representing the ICLS, with the model results reflecting the dynamic phases of such a system (e.g., soybean replaced by mixed-pasture with a rotational and intensive grazing system) and management operations (e.g., sowing, harvesting, mowing, fertilization, grazing events). Even though three different soybean varieties were sowing in the area in both seasons, the model presented consistent aboveground biomass and yield simulation results. Perhaps the difference between the rainfall distribution and the difference in the average air temperature among cycles have not influenced the differentiation of aboveground biomass production.
The VWC and its dynamics are of utmost importance since it affects plant growth, SOM mineralization, and, therefore, the C fluxes in the model. Moreover, in crops such as soybean, water stress impacts not only direct the plant growth but also triggers morpho-physiological responses mainly in the reproductive phase, demonstrated by premature senescence of leaves and flowers, death of pods and, consequently, grain yield reduction [37].
DayCent has received little assessment of CO2 predicted in tropical and ICLS conditions. This model was previously tested in Brazil in its capabilities to predict SOC changes [38], crop yield for common succession/rotation systems, N2O [15], and flows of CH4 [39], but not yet for CO2 fluxes, despite its importance in accurately simulating the conditions of a more intensive and complex system. DayCent overestimates the GHG over different environments [11][39][40].
A long-term ICLS implemented on sandy soil in a farm of western São Paulo state (SP) in Brazil, using a scheme of two years of soybean in the rainy season followed by pasture (dry season) and two years of only pasture, showed soybean yields ranging from 2.9 to 4.3 Mg ha−1, with the lowest values obtained under severe dry spells conditions [40]. These were considerably higher soybean yields than other farms in the same region in the conventional system (average of 1.8 Mg ha−1) [41], indicating that the increment in agricultural outputs may benefit from ICLSs.
5. Conclusions
With the simulation of two pastures grown simultaneously, it was possible to fill in a gap that had not yet been addressed with DayCent, especially when using a system as complex and intense as the ICLS. By using the aboveground biomass data to calibrate DayCent parameters, it was demonstrated that the model could be simplified according to the amount of data available, and it was possible to simulate plant growth, grain biomass, soil VWC, total SOC and CO2 fluxes, considering spatio-temporal evaluation and precision and accuracy metrics. Consistent simulations of grain biomass and plant growth data for other ICLSs were also found.
This entry is adapted from the peer-reviewed paper 10.3390/su14063517
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