Simulating forage crop production in a northern climate with the Integrated Farm System Model

Jégo, Guillaume; Rotz, C. Alan; Bélanger, Gilles; Tremblay, Gaëtan F.; Charbonneau, Édith; Pellerin, D.

doi:10.4141/cjps-2014-375

Cited by 18 publications

(21 citation statements)

References 56 publications

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“…Comparing IFSM simulations of feed production and intake, fossil energy use, and production costs with actual records for the US Meat Animal Research Center showed differences of less than 1% during the study year of 2011 (Rotz et al, 2013). Numerous other studies have verified the model's ability to represent feed crop production, animal performance, emissions, and other components of the model in dairy production (e.g., Belflower et al, 2012;Rotz et al, 2014;Bonifacio et al, 2015;Jego et al, 2015;Leytem et al, 2018).…”

Section: Modeling Proceduresmentioning

confidence: 87%

Regional environmental assessment of dairy farms

Rotz

Stout

Holly

et al. 2020

Journal of Dairy Science

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A comprehensive, yet in depth, assessment is needed of the environmental impacts of dairy farms at regional and national scales to better track improvements made by the industry. With Pennsylvania as an example, a method using process-level simulation and cradle-tofarm gate life cycle assessment was developed and used to assess important environmental footprints of dairy farms within a state. Representative dairy farms of various sizes and management practices throughout 7 regions of the state were simulated with the Integrated Farm System Model. Environmental footprints varied widely among farms, with this variation influenced primarily by soil characteristics and climate and secondarily by farm management. Therefore, prescriptive mitigation strategies for individual farms are more effective than uniform enforcement of specific strategies across the state. Footprints for the whole state were determined by totaling values among farms and regions based on the amounts of milk produced by each. Pennsylvania dairy farms were determined to emit 4,555 with an uncertainty of ±415 Gg of CO 2 equivalent of greenhouse gas with an intensity of 0.99 ± 0.09 kg of CO 2 equivalent/kg of fat-and protein-corrected milk (FPCM) produced. Fossil energy consumption was 12,324 ± 1,946 TJ or 2.69 ± 0.42 MJ/kg of FPCM. Blue (nonprecipitation) water consumption was 64.1 ± 13.5 Tg with an intensity of 14.0 ± 3.0 kg/kg of FPCM. A total of all forms of reactive N loss was 43.2 ± 5.0 Gg with an intensity of 9.4 ± 1.1 g/kg of FPCM. These metrics were equivalent to 1.6% of the greenhouse gas emissions, 0.4% of fossil energy use, and 0.8% of fresh water consumption reported for the state. Thus, greenhouse gas emissions, fossil energy use, and blue water use associated with dairy farm production are relatively small compared with total estimates for the state. Perhaps the greatest environmental concern is that of ammonia emission, where dairy farms accounted for about half the estimated emissions of the state. This method can be applied to assessments of the dairy industry at larger regional and national scales.

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Section: Modeling Proceduresmentioning

confidence: 87%

Regional environmental assessment of dairy farms

Rotz

Stout

Holly

et al. 2020

Journal of Dairy Science

Self Cite

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“…Uncertainties related to IFSM predictions were also low and in the same range as those reported for climate models. Jégo et al (2015) compared measured yield of an alfalfa-timothy mixture over 2 yr with simulated yield from IFSM, and found that the latter was underestimated by only 6%. The IFSM was also accurate in predicting each forage cut of the mixture within the growing season.…”

Section: Forage Mixture Yieldmentioning

confidence: 99%

“…This farm-scale model, developed for dairy, beef, and cash crop farms in the northeastern United States, allows the simulation of forage mixture yield and nutritive value as a process part of the whole-farm system. Jégo et al (2015) showed that IFSM can be used under current climate conditions in northern regions of North America, like eastern Canada, to simulate the yield and nutritive value of timothy and alfalfa, grown alone or in a mixture.…”

Section: Biometry Modeling and Statisticsmentioning

confidence: 99%

“…The crop growth parameters used in IFSM were previously calibrated and evaluated by Jégo et al (2015) for alfalfa and timothy with daily average optimum photosynthesis temperatures of 20.0 and 13.5 °C, minimum photosynthesis temperatures of 5 and 0°C, base photosynthetic rates of 25.0 and 17.1 mmol CO 2 m -2 s -1 , and radiation use efficiencies of 4.0 and 5.0 g DM MJ -1 of global radiation, respectively. Only the specific leaf area (leaf area/leaf dry mass ratio) of alfalfa was adjusted from 20 to 26 m 2 kg -1 leaf DM based on the findings of Bourgeois et al (1990), while the specific leaf area of timothy was kept at 30 m 2 kg -1 leaf DM.…”

Section: Integrated Farm System Modelmentioning

confidence: 99%

“…The TSI was calculated by linear interpolation of a triangular function in which no stress (TSI = 1.0) occurs at the optimum photosynthesis temperature [20°C for alfalfa (Jégo et al, 2015) and 13.5°C for timothy (Bonesmo and Bélanger, 2002)], and in which maximum stress (TSI = 0) occurs below the minimum photosynthesis temperature (5°C for alfalfa and 0°C for timothy) or above the maximum photosynthesis temperature (35°C for both species). The WSI (1.0 = no water stress) was calculated as the ratio of plant available water to critical soil moisture concentration.…”

Section: Integrated Farm System Modelmentioning

confidence: 99%

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Predicted Yield and Nutritive Value of an Alfalfa–Timothy Mixture under Climate Change and Elevated Atmospheric Carbon Dioxide

et al. 2016

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Climate change studies have oft en focused on individual forage species although legume-grass mixtures are predominant on dairy farms in northern areas of North America. We assessed the eff ect of (i) future climate conditions (temperature and precipitation) and elevated atmospheric CO 2 concentration ([CO 2 ]), separately and together, on yield of alfalfa (Medicago sativa L.) and timothy (Phleum pratense L.), grown alone or in mixture, and (ii) an adaptation strategy (timing and number of harvests) on future yield and nutritive value of an alfalfa-timothy mixture. Forage dry matter (DM) yield and nutritive value for two contrasting climate areas in eastern Canada were simulated with the Integrated Farm System Model over two future periods (2020-2049 and 2050-2079) using three climate models and two representative concentration pathways (RCP 4.5 and 8.5) of greenhouse gas emissions. Under projected future climate and without adaptation, annual forage yield of both species and the mixture increased in the colder area and decreased in the warmer area. In both areas, fi rst-cut yield increased due to faster growing degree-day accumulation, while regrowth yield decreased due to greater water and temperature stresses. Under elevated [CO 2 ], annual yield and the alfalfa percentage in the mixture increased. When combining climate change and elevated [CO 2 ], yield increased, except with the more drastic scenario (RCP 8.5, 2050(RCP 8.5, -2079 in the warmer area, and forage nutritive value was reduced. With adaptation, the mixture yield was increased from 5 to 35%, while nutritive value was generally maintained under all future scenarios, mostly because of additional cuts.

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Modeled performance of forage mixtures and annual crops grown in eastern Canada under climate change

et al. 2021

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Timothy (Phleum pratense L.) is expected to be more affected by climate change than other forage grasses. Therefore, alternatives to timothy, such as tall fescue [Schedonorus arundinaceus (Shreb.) Dumort.], meadow fescue [S. pratensis (Huds.) P. Beauv.], or meadow bromegrass (Bromus biebersteinii Roem. & Schult.) should be explored. Our objective was to simulate and compare the yield and nutritive value of four alfalfa (Medicago sativa L.)–grass mixtures and annual crops grown on two virtual dairy farms representative of eastern Canada under future climate conditions. The Integrated Farm System Model (IFSM) was used for these projections under the reference (1971–2000), near future (2020–2049), and distant future (2050–2079) climates for two climatically contrasting agricultural areas in eastern Canada (eastern Quebec; southwestern Quebec). In both future periods, annual forage dry matter (DM) yields of the four alfalfa–grass mixtures are projected to increase because of additional harvests, with greater DM yield increases projected in the colder area than in the warmer area. In both areas, the highest yield increase is projected for alfalfa–tall fescue mixture and the lowest for alfalfa–timothy mixture. The nutritive value of all mixtures should increase due to a greater proportion of alfalfa. In both areas, yields of silage and grain corn (Zea mays L.), and soybean [Glycine max (L.) Merr.] are projected to increase, but not those of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.). Tall fescue, meadow bromegrass, and meadow fescue are adequate alternatives to timothy grown in association with alfalfa under future climate conditions.

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Simulating forage crop production in a northern climate with the Integrated Farm System Model

Cited by 18 publications

References 56 publications

Regional environmental assessment of dairy farms

Regional environmental assessment of dairy farms

Predicted Yield and Nutritive Value of an Alfalfa–Timothy Mixture under Climate Change and Elevated Atmospheric Carbon Dioxide

Modeled performance of forage mixtures and annual crops grown in eastern Canada under climate change

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