Reimplementation and reuse of the Canegro model: From sugarcane to giant reed

Stella, Tommaso; Francone, Caterina; Yamaç, Sevim Seda; Ceotto, E.; Pagani, Valentina; Pilu, Roberto; Confalonieri, Roberto

doi:10.1016/j.compag.2015.02.009

Cited by 13 publications

(7 citation statements)

References 34 publications

(38 reference statements)

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“…The moderate requirements for nutrients and the noticeable tolerance for drought of giant reed [35] allowed, for this exploratory study, running the Arungro model under potential conditions for water and nutrients. Moreover, the low genetic variability of the species, derived from its agamic reproduction, allowed for running simulations using default values for parameters [33] and excluded the possibility of testing alternative genotypes as an adaptation strategy to the foreseen climate.…”

Section: Giant Reed Model and Parameterizationmentioning

confidence: 99%

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Are advantages from the partial replacement of corn with second-generation energy crops undermined by climate change? A case study for giant reed in northern Italy

Cappelli

Yamaç

Stella

et al. 2015

Biomass and Bioenergy

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Section: Giant Reed Model and Parameterizationmentioning

confidence: 99%

“…The Arungro model [33] was used for the simulation of giant reed full productive stands, i.e., the juvenile phase characterizing the season of crop establishment was not taken into account. The model calculates daily biomass accumulation as a balance between gross photosynthesis and growth/maintenance respiration.…”

Section: Giant Reed Model and Parameterizationmentioning

confidence: 99%

Are advantages from the partial replacement of corn with second-generation energy crops undermined by climate change? A case study for giant reed in northern Italy

Cappelli

Yamaç

Stella

et al. 2015

Biomass and Bioenergy

Self Cite

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“…This exploratory study was performed via the application of the biophysical Arungro model (rainfed conditions; [35]) at 500 m spatial resolution by combining the latest generation of crop growth models; climate change scenarios from the Intergovernmental Panel on Climate Change (IPCC); and databases with in-depth information on soil chemical/physical characteristics, marginality, and crop suitability to environment. The analysis was centered on the Catania and Bologna provinces, which are characterized by contrasting pedo-climatic conditions and well represented in terms of calibration dataset.…”

Section: Methodsmentioning

confidence: 99%

“…The Arungro model [35] was used for the simulation of giant reed development and growth, considering soil water balance and agricultural practices (i.e., transplanting and Concerning renewable energy production, in Italy there are 1681 biogas plants throughout the country, of which there are 762 for electricity production and 919 for the combined production of electricity and heat [38]. Seventy-five percent of materials used to feed the biogas plants derive from agriculture, whereas the remaining 25% are from municipal wastes.…”

Section: Model Description and Calibrationmentioning

confidence: 99%

“…The Arungro model [35] was used for the simulation of giant reed development and growth, considering soil water balance and agricultural practices (i.e., transplanting and cutting times). Arungro simulates daily net carbon fixation as a balance between gross photosynthesis and growth/maintenance respiration, depending on radiation interception and crop transpiration.…”

Section: Model Description and Calibrationmentioning

confidence: 99%

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Model-Based Assessment of Giant Reed (Arundo donax L.) Energy Yield in the Form of Diverse Biofuels in Marginal Areas of Italy

Cappelli

Ginaldi

Fanchini

et al. 2021

Land

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Giant reed is a promising perennial grass providing ligno-cellulosic biomass suitable to be cultivated in marginal lands (MLs) and converted into several forms of renewable energy. This study investigates how much energy, in the form of biomethane, bioethanol, and combustible solid, can be obtained by the cultivation of this species in marginal land of two Italian regions, via the spatially explicit application of the Arungro crop model. Arungro was calibrated in both rainfed/well-irrigated systems, under non-limiting conditions for nutrient availability. The model was then linked to a georeferenced database, with data on (i) current/future climate, (ii) agro-management, (iii) soil physics/hydrology, (iv) land marginality, and (v) crop suitability to environment. Simulations were run at 500 × 500 m spatial resolution in MLs of Catania (CT, Southern Italy) and Bologna (BO, Northern Italy) provinces, characterized by contrasting pedo-climates. At field scale, Arungro explained 85% of the year-to-year variability of measured carbon accumulation in aerial biomass. At the provincial level, simulated energy yields progressively increased from bioethanol, to biomethane, and finally to combustible solid, with average values of 92-115-264 GJ ha−1 in BO and 105-133-304 GJ ha−1 in CT. Mean energy yields estimated for 2030 remained unchanged compared to the baseline, although showing large heterogeneity across the study area (changes between −6/+15% in BO and −16/+15% in CT). This study provides site-specific indications on giant reed current productions, energy yields, and natural water consumption, as well as on their future trends and stability, ready-to-use for multiple stakeholders of the agricultural sector involved in bioenergy planning.

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Long-Term Yields of Switchgrass, Giant Reed, and Miscanthus in the Mediterranean Basin

et al. 2015

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Uncertainty in predictions of long-term yields of perennial grasses makes business plans untenable 21 in the short run. Long-term data across varied environments, including marginal lands, will help in 22 preventing uncertainty while providing farmers and entrepreneurs with sound information to 23 estimate reliable and affordable strategies on what, where and how long to grow perennial grasses. 24 In the present study, the long-term yields (11 to 22 years) of switchgrass (Panicum virgatum L.), 25 miscanthus (Miscanthus × giganteus Greef et Deuter) and giant reed (Arundo donax L.) grown in 26 northern and southern Mediterranean environments are reported. Switchgrass was grown in Greece 27 and northern Italy, giant reed in southern and northern Italy, and miscanthus in southern Italy. 28 Furthermore, lowland and upland switchgrass ecotypes were compared in Greece. Despite similar 29 biomass productions (9.8 and 10.0 Mg DM ha -1 for uplands and lowlands, respectively), the upland 30 ecotypes showed a significantly higher yield stability (CV of 24% and 32% for uplands and 31 lowlands, respectively) over a 17-year period. Biomass yield varied considerably across years and 32 locations; giant reed outperformed switchgrass under northern Italy environment (21.2 and 13.6 Mg 33 DM ha -1 for giant reed and switchgrass respectively). Annual yield of switchgrass was 30% higher 34 in the north than south Mediterranean; miscanthus showed intermediate production compared to 35 giant reed and switchgrass (average of 22 years) and a CV similar to switchgrass. In summary these 36 results evidence that multi-location, long-term trials are strongly needed to reduce uncertainties on 37 crop yield variability and provide more accurate data from which optimized socio-economic and 38 environmental predictions can be achieved.

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Reimplementation and reuse of the Canegro model: From sugarcane to giant reed

Cited by 13 publications

References 34 publications

Are advantages from the partial replacement of corn with second-generation energy crops undermined by climate change? A case study for giant reed in northern Italy

Are advantages from the partial replacement of corn with second-generation energy crops undermined by climate change? A case study for giant reed in northern Italy

Model-Based Assessment of Giant Reed (Arundo donax L.) Energy Yield in the Form of Diverse Biofuels in Marginal Areas of Italy

Long-Term Yields of Switchgrass, Giant Reed, and Miscanthus in the Mediterranean Basin

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