Skip to main content

Advertisement

SpringerLink
Log in

Integrated Assessment and Modeling of Agricultural Mechanization in Potato Production of Iran by Artificial Neural Networks

  • Full-Length Research Article
  • Published:
Agricultural Research Aims and scope Submit manuscript

Abstract

Mechanization is a concept and cannot be measured directly, so we used appropriate indicators in order to determine mechanization status for the first time in this field of study. In this paper, based on energy and power availability, four indicators, namely machinery energy ratio, mechanization index, productivity level of consumed power, and mechanization level, were selected for assessing agricultural mechanization in potato production of Iran using integrated assessment and modeling (IAM) to provide insight into the potential impacts of policy changes. To do an IAM in agricultural mechanization, we used pervasive analysis using more than 90 features in sample farms. This IAM is the first generalized model in agricultural mechanization. The main purpose of this study is presenting and showing capability of ANNs to model agricultural mechanization status and indicating best ANN model. Finally, a two hidden layer model with these features showed best performance: generalized feed-forward network with Levenberg Marquart learning rule and Bias Axon transfer function with 4–10 neurons in two hidden layers which have 27 input items for modeling four outputs. In this study, ANN models were introduced and applied to help IAM investigation which integrating production factors to have better knowledge about agricultural system of potato production in a wide region.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Abbreviations

ANN:

Artificial neural networks

Ax:

Axon (transfer function of ANN)

BA:

BiasAxon (transfer function of ANN)

CG:

ConjugateGradient (learning rule of ANN)

DBD:

DeltaBarDelta (learning rule of ANN)

GFF:

Generalized feed-forward

IAM:

Integrated assessment and modeling

LA:

LinearAxon (transfer function of ANN)

LM:

LevenbergMarquar (learning rule of ANN)

LSA:

LinearSigmoidAxon (transfer function of ANN)

LTA:

LinearTanhAxon (transfer function of ANN)

MAE:

Mean absolute error

MER:

Machinery energy ratio

MI:

Mechanization index

ML:

Mechanization level

MLP:

Multi layer perceptron

MoM:

Momentum (learning rule of ANN)

MSE:

Mean squared Error

NMSE:

Normalized mean squared error

PLCP:

Productivity level of consumed power

QP:

Quickprop (learning rule of ANN)

R 2 :

Coefficent of determination

SA:

SigmoidAxon (transfer function of ANN)

SMA:

SoftMaxAxon (transfer function of ANN)

TA:

TanhAxon (transfer function of ANN)

References

  1. Abdullakasim W, Koike M, Takigawa T, Hasegawa H, Yoda A, Bahalayodhin B (2005) Agroproductivo del estado de Guanajuato. SICOPOT-GTO. Guanajuato. México (CD-ROM In Spanish)

  2. Andrade P, Jenkins BM (2003) Identification of patterns of farm equipment utilization in two agricultural regions of central and northern Mexico. Agric Eng Int 5:276–288

    Google Scholar 

  3. Anonymous (2012). www.neurosolutions.com/products/nsexcel/. Retrieved at 2012-01-12

  4. Chester J (1993) Neural networks: a tutorial. Prentice Hall, New York

    Google Scholar 

  5. Collado M, Calderón E (2000) Energía de utilizada para la producción agrícola en el estado de Guanajuato: Cuantificación y análisis. Memorias del III Congreso Latinoamericano de Ingeniería Agrícola. Guanajuato. México (In Spanish)

  6. Drummond ST, Sudduth KA, Joshi A, Birrel SJ, Kitchen NR (2003) Statistical and neural methods for site-specific yield prediction. Trans ASAE 46(1):5–14

    Article  Google Scholar 

  7. Ewert F, van Ittersum MK, Bezlepkina I, Therond O, Andersen E, Belhouchette H, Bockstaller C, Brouwer F, Heckelei T, Janssen S, Knapen R, Kuiper M, Louhichi K, Alkan Olsson J, Turpin N, Wery J, Wien JE, Wolf J (2009) A methodology for enhanced flexibility of integrated assessment in agriculture. Environ Sci Policy 12(2009):546–561

    Article  Google Scholar 

  8. Fischer G, Shah MN, Tubiello F, van Velhuizen H (2005) Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080. Philos Trans R Soc B 360(1463):2067–2083

    Article  Google Scholar 

  9. García H, García OJ, Ramírez A (2005) INIFAP. Sistema de consulta del potencial

  10. Gifford RC, Rijk AG (1980) Guidelines for agricultural mechanization strategy in development. Economic and Social Commission for Asia and the Pacific (ESCAP), Regional Network for Agricultural Machinery

  11. Giles GW (1975) The reorientation of agricultural mechanization for the developing countries. FAO Report on Effect of Farm Mechanization on Production and Employment. Food and Agricultural Organization (FAO), Rome, Italy

  12. Grübler A (1998) Technology and global change. Cambridge University Press, Cambridge

    Book  Google Scholar 

  13. Harris G (2002) Integrated assessment and modelling—science for sustainability. In: Costanza R, Joergensen SE (eds) Understanding and Solving Environmental Problems in the 21st Century. Elsevier, Amsterdam, pp 5–17

    Chapter  Google Scholar 

  14. Höchtl F, Lehringer S, Konold W (2005) Wilderness: what it means when it becomes a reality—a case study from the southwestern Alps. Landsc Urban Plan 70:85–95

    Article  Google Scholar 

  15. Holzschuh A, Steffan-Dewenter I, Tscharntke T (2010) How do landscape composition and configuration, organic farming and fallow strips affect the diversity of bees, wasps and their parasitoids? J Anim Ecol 79:491–500

    Article  PubMed  Google Scholar 

  16. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  17. Jongman RHG (2002) Homogenisation and fragmentation of the European landscape: ecological consequences and solutions. Landsc Urban Plan 58:211–221

    Article  Google Scholar 

  18. Klimek S, Richtergen A, Kemmermann A, Hofmann M, Isselstein J (2007) Plant species richness and composition in managed grasslands: the relative importance of field management and environmental factors. Biol Conserv 134:559–570

    Article  Google Scholar 

  19. Maurer K, Weyand A, Fischer M, Stöcklin J (2006) Old cultural traditions, in addition to land use and topography, are shaping plant diversity of grasslands in the Alps. Biol Conserv 130:438–446

    Article  Google Scholar 

  20. NCAER (1981) Implication of tractorisation for farm employment, productivity and income. National Council of Applied Economic Research, Parisila Bhawan, New Delhi

    Google Scholar 

  21. Oida A (2000) Agricultural machinery management using personal computer (Text & Practice). Agricultural Machinery Management Course organized by Japan International Cooperation Agency, Japan

  22. Parker P, Letcher R, Jakeman A, Beck MB, Harris G, Argent RM, Hare M, Pahl-Wostl C, Voinov A, Janssen M, Sullivan P, Scoccimarro M, Friend A, Sonnenshein M, Baker D, Matejicek L, Odulaja D, Deadman P, Lim K, Larocque G, Tarikhi P, Fletcher C, Put A, Maxwell T, Charles A, Breeze H, Nakatani N, Mudgal S, Naito W, Osidele O, Eriksson I, Kautsky U, Kautsky E, Naeslund B, Kumblad L, Park R, Maltagliati S, Girardin P, Rizzoli A, Mauriello D, Hoch R, Pelletier D, Reilly J, Olafsdottir R, Bin S (2002) Progress in integrated assessment and modelling. Environ Model Softw 17(3):209–217

    Article  Google Scholar 

  23. Pearce D (2005) What constitutes a good agro-environmental policy evaluation? In: OECD (ed) Evaluating agri-environmental policies. design, practice and results. OECD, Paris, pp 71–97

    Chapter  Google Scholar 

  24. Rijk AG (1989) Agricultural mechanisation policy and strategy—the case of Thailand. Asian Productivity Organisation, Tokyo

    Google Scholar 

  25. Ripoll-Bosch R, Díez-Unquera B, Ruiz R, Villalba D, Molina E, Joy M, Olaizola A, Bernués A (2012) An integrated sustainability assessment of mediterranean sheep farms with different degrees of intensification. Agric Syst 105:46–56

    Article  Google Scholar 

  26. Roschewitz I, Gabriel D, Tscharntke T, Thies C (2005) The effects of landscape complexity on arable weed species diversity in organic and conventional farming. J Appl Ecol 42:873–882

    Article  Google Scholar 

  27. Rotmans J, van Asselt M (1996) Integrated assessment:growing child on its way to maturity—an editorial essay. Clim Chang 34:327–336

    Article  Google Scholar 

  28. Rotmans J, Deboois H, Swart RJ (1990) An integrated model for the assessment of the greenhouse-effect—the Dutch Approach. Clim Chang 16(3):331–356

    Article  Google Scholar 

  29. Schmitzberger I, Wrbka T, Steurer B, Aschenbrenner G, Peterseil J, Zechmeister HG (2005) How farming styles influence biodiversity maintenance in Austrian agricultural landscapes. Agric Ecosyst Environ 108:274–290

    Article  Google Scholar 

  30. Schönhart M, Schauppenlehner T, Schmid E, Muhar A (2011) Integration of bio-physical and economic models to analyze management intensity and landscape structure effects at farm and landscape level. Agric Syst 104:122–134

    Article  Google Scholar 

  31. Sims B (1987) Mecanización para el pequeño agricultor. Secretaría de Agricultura y Recursos Hidráulicos. Instituto Nacional de Investigaciones Forestales y Agropecuarias. México, D.F.México. (In Spanish)

  32. Singh G (1997) Data book on mechanization and agro processing in India after independence. Technical Bulletin CIAE/97/71. Central Institute of Agricultural Engineering, Nabi Bagh, Bhopal

    Google Scholar 

  33. Singh G (2000) Modernisation of agriculture in India (part I)—farm mechanization. Agricultural situation in India. Ministry of Agriculture, New Delhi

    Google Scholar 

  34. Singh G, Chandra H (2002) Production and economic factors growth in Indian agriculture. Technical Bulletin No. CIAE/2002/91. Central Institute of Agricultural Engineering, Nabi Bagh, Bhopal

    Google Scholar 

  35. Stoate C, Báldi A, Beja P, Boatman N, Herzon I, van Doorn A, de Snoo G, Rakosy L, Ramwell C (2009) Ecological impacts of early 21st century agricultural change in Europe—a review. J Environ Manage 91:22–46

    Article  CAS  PubMed  Google Scholar 

  36. Tol RSJ (2006) Integrated assessment modeling. working papers: FNU-102, Research unit sustainability and global change. Hamburg University, Hamburg

  37. Tscharntke T, Klein AM, Kruess A, Steffan-Dewenter I, Thies C (2005) Landscape perspectives on agricultural intensification and biodiversity–ecosystem service management. Ecol Lett 8:857–874

    Article  Google Scholar 

  38. Van Cauwenbergh N, Biala K, Bielders C, Brouckaert V, Franchois L, Garcia Cidad V, Hermy M, Mathijs E, Muys B, Reijnders J, Sauvenier X, Valckx J, Vanclooster M, Van der Veken B, Wauters E, Peeters A (2007) SAFE—a hierarchical framework for assessing the sustainability of agricultural systems. Agric Ecosyst Environ 120(2–4):229–242

    Article  Google Scholar 

  39. van der Werf HMG, Petit J (2002) Evaluation of the environmental impact of agriculture at the farm level: a comparison and analysis of 12 indicator-based methods. Agric Ecosyst Environ 93:131–145

    Article  Google Scholar 

  40. van Ittersum MK, Ewert F, Heckelei T, Wery J, Alkan Olsson J, Andersen E, Bezlepkina I, Brouwer F, Donatelli M, Flichman G, Olsson L, Rizzoli AE, van der Wal T, Wien JE, Wolf J (2008) Integrated assessment of agricultural systems—a component-based framework for the European Union (SEAMLESS). Agric Syst 96:150–165

    Article  Google Scholar 

  41. Velthof GL, Oudendag DA, Oenema O (2007) Development and application of the integrated nitrogen model MITERRAEUROPE. Alterra Report. Alterra, Wageningen

  42. Verburg P, Eickhout B, van Meijl H (2008) A multi-scale, multi-model approach for analyzing the future dynamics of European land use. Ann Reg Sci 42(1):57–77

    Article  Google Scholar 

  43. Zangeneh M, Omid M, Akram A (2010) A comparative study on energy use and cost analysis of potato production under different farming technologies in Hamadan province of Iran. Energy 35:1–7

    Article  Google Scholar 

  44. Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, School of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

    Morteza Zangeneh, Mahmoud Omid & Asadolah Akram

Authors
  1. Morteza Zangeneh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahmoud Omid
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asadolah Akram
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Morteza Zangeneh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zangeneh, M., Omid, M. & Akram, A. Integrated Assessment and Modeling of Agricultural Mechanization in Potato Production of Iran by Artificial Neural Networks. Agric Res 4, 283–302 (2015). https://doi.org/10.1007/s40003-015-0160-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40003-015-0160-z

Keywords

Search

Navigation