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Brilli L, Bechini L, Bindi M, Carozzi M, Cavalli D, Conant R, Dorich CD, Doro L, Ehrhardt F, Farina R, Ferrise R, Fitton N, Francaviglia R, Grace P, Iocola I, Klumpp K, Léonard J, Martin R, Massad RS, Recous S, Seddaiu G, Sharp J, Smith P, Smith WN, Soussana JF, Bellocchi G. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes. Sci Total Environ 2017; 598:445-470. [PMID: 28454025 DOI: 10.1016/j.scitotenv.2017.03.208] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Revised: 03/21/2017] [Accepted: 03/22/2017] [Indexed: 05/21/2023]
Abstract
Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.
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Affiliation(s)
- Lorenzo Brilli
- Università degli Studi di Firenze, Department of Agri-Food Production and Environmental Sciences, 50144 Florence, Italy; IBIMET-CNR, Via Caproni 8, 50145 Firenze, Italy.
| | - Luca Bechini
- Università degli Studi di Milano, Department of Agricultural and Environmental Sciences, Milan, Italy
| | - Marco Bindi
- Università degli Studi di Firenze, Department of Agri-Food Production and Environmental Sciences, 50144 Florence, Italy
| | - Marco Carozzi
- INRA, AgroParisTech, UMR1402 EcoSys, 78850 Thiverval-Grignon, France
| | - Daniele Cavalli
- Università degli Studi di Milano, Department of Agricultural and Environmental Sciences, Milan, Italy
| | - Richard Conant
- NREL, Colorado State University, Fort Collins, CO 80523, USA
| | | | - Luca Doro
- Desertification Research Centre, Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy; Texas A&M AgriLife Research, Blackland Research & Extension Center, Temple, (TX), USA
| | | | - Roberta Farina
- CREA-RPS, Research Centre for the Soil-Plant System, Via della Navicella 2-4, 00184 Roma, Italy
| | - Roberto Ferrise
- Università degli Studi di Firenze, Department of Agri-Food Production and Environmental Sciences, 50144 Florence, Italy
| | - Nuala Fitton
- Institute of Biological and Environmental Sciences, University of Aberdeen, St Machar Drive, AB24 3UU Aberdeen, UK
| | - Rosa Francaviglia
- CREA-RPS, Research Centre for the Soil-Plant System, Via della Navicella 2-4, 00184 Roma, Italy
| | - Peter Grace
- Queensland University of Technology, Brisbane, Australia
| | - Ileana Iocola
- Desertification Research Centre, Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy
| | | | - Joël Léonard
- INRA, UR 1158 AgroImpact, site de Laon, F-02000 Barenton-Bugny, France
| | | | | | | | - Giovanna Seddaiu
- Desertification Research Centre, Department of Agricultural Sciences, University of Sassari, 07100 Sassari, Italy
| | - Joanna Sharp
- New Zealand Institute for Plant and Food Research, 7608 Lincoln, New Zealand
| | - Pete Smith
- Institute of Biological and Environmental Sciences, University of Aberdeen, St Machar Drive, AB24 3UU Aberdeen, UK
| | - Ward N Smith
- Agriculture and Agri-Food Canada, Ottawa, Ontario K1A 0C6, Canada
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Pereira ABD, Utsumi SA, Dorich CD, Brito AF. Integrating spot short-term measurements of carbon emissions and backward dietary energy partition calculations to estimate intake in lactating dairy cows fed ad libitum or restricted. J Dairy Sci 2015; 98:8913-25. [PMID: 26506553 DOI: 10.3168/jds.2015-9659] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Accepted: 08/28/2015] [Indexed: 11/19/2022]
Abstract
The objective of this study was to use spot short-term measurements of CH4 (QCH4) and CO2 (QCO2) integrated with backward dietary energy partition calculations to estimate dry matter intake (DMI) in lactating dairy cows. Twelve multiparous cows averaging 173±37d in milk and 4 primiparous cows averaging 179±27d in milk were blocked by days in milk, parity, and DMI (as a percentage of body weight) and, within each block, randomly assigned to 1 of 2 treatments: ad libitum intake (AL) or restricted intake (RI=90% DMI) according to a crossover design. Each experimental period lasted 22d with 14d for treatments adaptation and 8d for data and sample collection. Diets contained (dry matter basis): 40% corn silage, 12% grass-legume haylage, and 48% concentrate. Spot short-term gas measurements were taken in 5-min sampling periods from 15 cows (1 cow refused sampling) using a portable, automated, open-circuit gas quantification system (GreenFeed, C-Lock Inc., Rapid City, SD) with intervals of 12h between the 2daily samples. Sampling points were advanced 2h from a day to the next to yield 16 gas samples per cow over 8d to account for diurnal variation in QCH4 and QCO2. The following equations were used sequentially to estimate DMI: (1) heat production (MJ/d)=(4.96 + 16.07 ÷ respiratory quotient) × QCO2; respiratory quotient=0.95; (2) metabolizable energy intake (MJ/d)=(heat production + milk energy) ± tissue energy balance; (3) digestible energy (DE) intake (MJ/d)=metabolizable energy + CH4 energy + urinary energy; (4) gross energy (GE) intake (MJ/d)=DE + [(DE ÷ in vitro true dry matter digestibility) - DE]; and (5) DMI (kg/d)=GE intake estimated ÷ diet GE concentration. Data were analyzed using the MIXED procedure of SAS (SAS Institute Inc., Cary, NC) and Fit Model procedure in JMP (α=0.05; SAS Institute Inc.). Cows significantly differed in DMI measured (23.8 vs. 22.4kg/d for AL and RI, respectively). Dry matter intake estimated using QCH4 and QCO2 coupled with dietary backward energy partition calculations (Equations 1 to 5 above) was highest in cows fed for AL (22.5 vs. 20.2kg/d). The resulting R(2) were 0.28 between DMI measured and DMI estimated by gaseous measurements, and 0.36 between DMI measured and DMI predicted by the National Research Council model (2001). Results showed that spot short-term measurements of QCH4 and QCO2 coupled with dietary backward estimations of energy partition underestimated DMI by 7.8%. However, the approach proposed herein was able to significantly discriminate differences in DMI between cows fed for AL or RI.
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Affiliation(s)
- A B D Pereira
- Department of Biological Sciences, University of New Hampshire, Durham 03824
| | - S A Utsumi
- Kellogg Biological Station, Michigan State University, Hickory Corners 49060
| | - C D Dorich
- Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham 03824
| | - A F Brito
- Department of Biological Sciences, University of New Hampshire, Durham 03824.
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