Advertisement

Agroforestry Systems

, Volume 89, Issue 5, pp 933–942 | Cite as

Productivity of poplar short rotation coppice in an alley-cropping agroforestry system

  • Justine Lamerre
  • Kai-Uwe Schwarz
  • Maren Langhof
  • Georg von Wühlisch
  • Jörg-Michael Greef
Article

Abstract

In temperate regions, short rotation woody crops cultivated as tree strips in alley-cropping agroforestry systems (ACS) can provide economic benefits by producing an additional commodity, and environmental benefits, e.g., soil erosion control, protection against evaporation and increased biodiversity. Moreover, compared to agrisivilcutural systems incorporating noble trees, ACS with short rotation coppice (SRC)-strips provide periodically high energy outputs and could help to answer environment and energetic political objectives in Germany. However, limited data are available in such systems concerning biomass production of different strip designs. An ACS incorporating poplar SRC has been established near Braunschweig in Lower Saxony (Germany) in 2008. Two harvest cycles (a coppiced 3-year rotation cycle and a un-coppiced 6-year rotation cycle) and two strip designs (“SRC”: 6 poplar rows; “Combined”: 4 poplar rows and 1 aspen row in the centre) were compared. Diameters at breast height, tree heights, shoot numbers and mortality rates were measured to describe growth and estimate yield of outer and middle poplar rows within a tree strip. Concerning the 3-year rotation cycle, higher numbers of shoots per tree as well as higher biomass yields compared to the control field were measured in outer rows, both leeward and windward. With the 6-year rotation cycle, all leeward rows and the middle rows of the combined design showed larger diameters and higher biomass yields. Middle rows of the SRC design, in both rotation cycles, revealed a quicker height growth than outer rows, but a reduced biomass production. Both rotation cycles showed similar yearly biomass production. The results can contribute to improve the design of poplar SRC-strips in ACS in order to optimize biomass production. We recommend reducing the number of rows within SRC-strips, while increasing their total length. Further research is however needed to determine effects of increased biomass in outer tree rows on adjacent crop fields, which influences the whole system productivity.

Keywords

Alley-cropping Short rotation coppice Poplar Biomass Growth 

Abbreviations

SRC

Short rotation coppice

GHG

Greenhouse gases

ACS

Alley-Cropping agroforestry system

DBH

Diameter at breast height

3y-RC

3-year rotation cycle

6y-RC

6-year rotation cycle

References

  1. Armstrong A, Johns C, Tubby I (1999) Effects of spacing and cutting cycle on the yield of poplar grown as an energy crop. Biomass Bioenergy 17:305–314. doi: 10.1016/S0961-9534(99)00054-9 CrossRefGoogle Scholar
  2. Auclair D, Bouvarel L (1992) Influence of spacing and short rotations on Populus trichocarpa × deltoides coppice. Can J For Res 22:541–548. doi: 10.1139/x92-071 CrossRefGoogle Scholar
  3. Baltaxe R (1967) Air flow patterns in the lee of model windbreaks. Arch für Meteorol Geophys und Bioklimatologie Ser B 15:287–312. doi: 10.1007/BF02243857 CrossRefGoogle Scholar
  4. Benomar L, DesRochers A, Larocque GR (2012) The effects of spacing on growth, morphology and biomass production and allocation in two hybrid poplar clones growing in the boreal region of Canada. Trees 26:939–949. doi: 10.1007/s00468-011-0671-6 CrossRefGoogle Scholar
  5. BMELV (2012) Pappeln und Weiden in Deutschland: Bericht der Nationalen Pappelkommission. Zeitraum 2008-2011. BonnGoogle Scholar
  6. Böhme D, Nick-Leptin J (2013) Erneuerbare Energien in Zahlen. Nationale und internationale Entwicklung, 1st ed. BerlinGoogle Scholar
  7. Brandle JR, Hodges L, Zhou XH (2004) Windbreaks in North American agricultural systems. Agrofor Syst 61:65–78. doi: 10.1023/B:AGFO.0000028990.31801.62 Google Scholar
  8. Cannell MGR (1980) Productivity of closely-spaced young poplar on agricultural soils in Britain. Forestry 53:1–21. doi: 10.1093/forestry/53.1.1 CrossRefGoogle Scholar
  9. Ceulemans R, Deraedt W (1999) Production physiology and growth potential of poplars under short-rotation forestry culture. For Ecol Manag 121:9–23. doi: 10.1016/S0378-1127(98)00564-7 CrossRefGoogle Scholar
  10. Curlin JW (1967) Clonal differences in yield response of Populus deltoides to nitrogen fertilization. Soil Sci Soc Am J 31:276. doi: 10.2136/sssaj1967.03615995003100020035x CrossRefGoogle Scholar
  11. DeBell DS, Clendenen GW, Harrington CA, Zasada JC (1996) Tree growth and stand development in short-rotation Populus plantings: 7-year results for two clones at three spacings. Biomass Bioenergy 11:253–269. doi: 10.1016/0961-9534(96)00020-7
  12. Deckmyn G, Laureysens I, Garcia J et al (2004) Poplar growth and yield in short rotation coppice: model simulations using the process model SECRETS. Biomass Bioenergy 26:221–227. doi: 10.1016/S0961-9534(03)00121-1 CrossRefGoogle Scholar
  13. Dixon M, Grace J (1984) Effect of wind on the transpiration of young trees. Ann Bot 53:811–819Google Scholar
  14. Edenhofer O, Pichs-Madruga R, Sokona Y et al (2011) IPCC, 2011: summary for policymakers. In: von Stechow C (ed) IPCC renewable energy sources and climate change mitigation Cambridge. Cambridge Univ. Press, Cambridge, pp 15–26CrossRefGoogle Scholar
  15. Farmer RJ (1963) Effect of light intensity on growth of Populus tremuloides cuttings under two temperature regimes. Ecology 44:409–411CrossRefGoogle Scholar
  16. Federal Ministry of Food and Agriculture (2007) Use of biomass for energy generation. Recommendations to policy makers. http://www.bmel.de/EN/Ministry/Scientific-Advisory-Boards/_Texte/UseOfBiomassForEnergyGeneration.html
  17. Gamble JD, Johnson G, Sheaffer CC et al (2014) Establishment and early productivity of perennial biomass alley cropping systems in Minnesota, USA. Agrofor Syst 88:75–85. doi: 10.1007/s10457-013-9657-2 CrossRefGoogle Scholar
  18. Grace J (1988) 3. Plant response to wind. Agric Ecosyst Environ 22–23:71–88. doi: 10.1016/0167-8809(88)90008-4 CrossRefGoogle Scholar
  19. Gruenewald H, Brandt BKV, Schneider BU et al (2007) Agroforestry systems for the production of woody biomass for energy transformation purposes. Ecol Eng 29:319–328. doi: 10.1016/j.ecoleng.2006.09.012 CrossRefGoogle Scholar
  20. Guidi W, Piccioni E, Ginanni M, Bonari E (2008) Bark content estimation in poplar (Populus deltoides L.) short-rotation coppice in Central Italy. Biomass Bioenergy 32:518–524. doi: 10.1016/j.biombioe.2007.11.012 CrossRefGoogle Scholar
  21. Harvey M, Pilgrim S (2011) The new competition for land: food, energy, and climate change. Food Policy 36:S40–S51. doi: 10.1016/j.foodpol.2010.11.009 CrossRefGoogle Scholar
  22. Heilman PE, Fu-Guang X (1993) Influence of nitrogen on growth and productivity of short-rotation Populus trichocarpa × Populus deltoides hybrids. Can J For Res 23:1863–1869. doi: 10.1139/x93-236 CrossRefGoogle Scholar
  23. Hertel T, Steinbuks J, Baldos U (2013) Competition for land in the global bioeconomy. Agric Econ 44:129–138. doi: 10.1111/agec.12057 CrossRefGoogle Scholar
  24. Herve C, Ceulemans R (1996) Short-rotation coppiced vs non-coppiced poplar: a comparative study at two different field sites. Biomass Bioenergy 11:139–150. doi: 10.1016/0961-9534(96)00028-1 CrossRefGoogle Scholar
  25. Hofmann-Schielle C, Jug A, Makeschin F, Rehfuess KE (1999) Short-rotation plantations of balsam poplars, aspen and willows on former arable land in the Federal Republic of Germany. I. Site–growth relationships. Forest Ecol Manag 121:41–55. doi: 10.1016/S0378-1127(98)00555-6 CrossRefGoogle Scholar
  26. Hothorn T, Bretz F, Westfall P (2008) Simultaneous Inference in General Parametric Models. Biometr J 50:346–363CrossRefGoogle Scholar
  27. Hytönen J, Lumme I, Törmälä T (1987) Comparison of methods for estimating willow biomass. Biomass 14:39–49. doi: 10.1016/0144-4565(87)90021-7 CrossRefGoogle Scholar
  28. Kauter D, Lewandowski I, Claupein W (2003) Quantity and quality of harvestable biomass from Populus short rotation coppice for solid fuel use—a review of the physiological basis and management influences. Biomass Bioenergy 24:411–427. doi: 10.1016/S0961-9534(02)00177-0 CrossRefGoogle Scholar
  29. Liesebach M, von Wuehlisch G, Muhs H-J (1999) Aspen for short-rotation coppice plantations on agricultural sites in Germany: effects of spacing and rotation time on growth and biomass production of aspen progenies. Forest Ecol Manag 121:25–39. doi: 10.1016/S0378-1127(98)00554-4 CrossRefGoogle Scholar
  30. Liu Z, Dickmann DI (1992) Responses of two hybrid Populus clones to flooding, drought, and nitrogen availability. I. Morphology and growth. Can J Bot 70:2265–2270. doi: 10.1139/b92-281 CrossRefGoogle Scholar
  31. Nair PKR (1993) An introduction to agroforestry. Kluwer Aca. Kluwer Acad. Publ, DordrechtCrossRefGoogle Scholar
  32. Nassi o di Nasso N, Guidi GW, Guidi GW, Ragaglini G et al (2010) Biomass production and energy balance of a 12-year-old short-rotation coppice poplar stand under different cutting cycles. GCB Bioenergy 2:89–97. doi: 10.1111/j.1757-1707.2010.01043.x CrossRefGoogle Scholar
  33. Otto S, Loddo D, Zanin G (2010) Weed-poplar competition dynamics and yield loss in Italian short-rotation forestry. Weed Res 50:153–162. doi: 10.1111/j.1365-3180.2010.00763.x CrossRefGoogle Scholar
  34. Pinheiro J, Bates D, DebRoy S, et al. (2014) nlme: Linear and Nonlinear Mixed Effects Models. R Packag version 31-117 http://cran.r-project.org/package=nlme
  35. Quinkenstein A, Wöllecke J, Böhm C et al (2009) Ecological benefits of the alley cropping agroforestry system in sensitive regions of Europe. Environ Sci Policy 12:1112–1121. doi: 10.1016/j.envsci.2009.08.008 CrossRefGoogle Scholar
  36. R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing http://www.r-project.org
  37. Sage R (1999) Weed competition in willow coppice crops: the cause and extent of yield losses. Weed Res 39:399–411. doi: 10.1046/j.1365-3180.1999.00154.x CrossRefGoogle Scholar
  38. Skaug H, Fournier D, Bolker B et al (2012) AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim Methods Softw 27:233–249CrossRefGoogle Scholar
  39. Taylor PJ, Nuberg IK, Hatton TJ (2001) Enhanced transpiration in response to wind effects at the edge of a blue gum (Eucalyptus globulus) plantation. Tree Physiol 21:403–408CrossRefPubMedGoogle Scholar
  40. Tsonkova P, Böhm C, Quinkenstein A, Freese D (2012) Ecological benefits provided by alley cropping systems for production of woody biomass in the temperate region: a review. Agrofor Syst 85:133–152. doi: 10.1007/s10457-012-9494-8 CrossRefGoogle Scholar
  41. Valentine J, Clifton-Brown J, Hastings A et al (2012) Food versus fuel: the use of land for lignocellulosic “next generation” energy crops that minimize competition with primary food production. GCB Bioenergy 4:1–19. doi: 10.1111/j.1757-1707.2011.01111.x CrossRefGoogle Scholar
  42. Verwijst T, Telenius B (1999) Biomass estimation procedures in short rotation forestry. For Ecol Manag 121:137–146. doi: 10.1016/S0378-1127(98)00562-3 CrossRefGoogle Scholar
  43. Wang Z, MacFarlane DW (2012) Evaluating the biomass production of coppiced willow and poplar clones in Michigan, USA, over multiple rotations and different growing conditions. Biomass Bioenergy 46:380–388. doi: 10.1016/j.biombioe.2012.08.003 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Julius Kühn-Institut for Crop and Soil ScienceBrunswickGermany
  2. 2.Thünen Institute for Forest GeneticsGrosshansdorfGermany

Personalised recommendations