Skip to main content


Log in

Farmers’ preference for cropping systems and the development of sustainable intensification: a choice experiment approach

  • Research Article
  • Published:
Review of Agricultural, Food and Environmental Studies Aims and scope Submit manuscript


Sustainable intensification (SI) of farming systems aims to increase food production from existing farmland in ways that have a lower environmental impact and maintain the food production capacity over time. SI embraces a set of diverse agricultural technologies that share a common feature: their adoption is dependent on the interactions between farmers’ decision-making processes, locally specific agro-ecological conditions, and the traits of the technology itself. There are concerns about the sustainability of the maize mono-cropping systems that are in use in Laosc today. Therefore, we used discrete choice experiments (DCE) to explore the potential adoption or alternative agricultural systems. We analyse the heterogeneity of farmers’ preferences and willingness to pay for different cropping system attributes using a mixed logit model, and we discuss the possible drivers and barriers to the adoption of these more sustainable options. The results suggest the existence of four types of farmers: “fertility-minded”, “factor-constrained”, “maximisers”, and “risk-averse”. Each type of farmers was likely to react differently to the proposed sustainable intensification techniques. Overall, the DCE appeared to be an efficient tool to elicit the diversity of farmer preferences in an agricultural region and for fine-tuning strategies for successful research and development of sustainable intensification.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others


  1. The exact sentence used by the moderator was “I know a perfect upland crop, what question would you like to ask me, in order to know if you would decide to grow it or not?”

  2. The full survey with farmers included a Best-Worst Scaling experiment to obtain the ranking of farm-level management priorities that we do not report in this paper, and the choice experiment for the choice of cropping systems analysed in this paper. It was felt that another set of experiments would have generated respondent fatigue, while not providing sufficient additional information at this stage of the research project.

  3. As we were looking for homogenous groups of preferences, we could use an alternative method based on a latent class logit (LCM) model. However, as shown in Annex C, we did not find any compelling advantages in using the LCM. As the LCM does not take into account the additional variance potentially associated with the new alternatives (the EC), we opted for this two-step approach.

  4. MRS calculations using the coefficients of the mixed logit model are also included in Table 1 of Annex D. Results showed that the MRS were of the same magnitude but higher when using the coefficients of the mixed logit model. For our discussion, we opted for the more conservative figures obtained with the CL model.


  • Adamowicz, W., Boxall, P., Williams, M., & Louviere, J. (1998). Stated preference approaches for measuring passive use values: choice experiments and contingent valuation. American Journal of Agricultural Economics, 80(1), 64–75.

    Article  Google Scholar 

  • Affholder, F., Jourdain, D., Quang, D. D., Tuong, T. P., Morize, M., & Ricome, A. (2010). Constraints to farmers’ adoption of direct-seeding mulch-based cropping systems: a farm scale modeling approach applied to the mountainous slopes of Vietnam. Agricultural Systems, 103(1), 51–62.

    Article  Google Scholar 

  • Alary, V., Nefzaoui, A., & Jemaa, M. B. (2007). Promoting the adoption of natural resource management technology in arid and semi-arid areas: modelling the impact of spineless cactus in alley cropping in Central Tunisia. Agricultural Systems, 94(2), 573–585.

    Article  Google Scholar 

  • Andersson, M., Engvall, A., & Kokko, A. (2007). Regional development in Lao PDR: growth patterns and market integration. Working Paper 234. Stockholm: Stockholm School of Economics.

  • Barham, B. L., Chavas, J.-P., Fitz, D., Ríos-Salas, V., & Schechter, L. (2015). Risk, learning, and technology adoption. Agricultural Economics, 46(1), 11–24.

    Article  Google Scholar 

  • Birol, E., Villalba, E. R., & Smale, M. (2009). Farmer preferences for milpa diversity and genetically modified maize in Mexico: a latent class approach. Environment and Development Economics, 14(4), 521–540.

    Article  Google Scholar 

  • Bocquého, G., Jacquet, F., & Reynaud, A. (2013). Expected utility or prospect theory maximisers? Assessing farmers’ risk behaviour from field-experiment data. European Review of Agricultural Economics, 41(1), 135–172.

    Article  Google Scholar 

  • Boussard, J.-M. (1969). The introduction of risk into a programming model: different criteria and the actual behavior of farmers. European Economic Review, 1(1), 92–121.

    Article  Google Scholar 

  • Campbell, B. M., Thornton, P., Zougmoré, R., van Asten, P., & Lipper, L. (2014). Sustainable intensification: what is its role in climate smart agriculture? Current Opinion in Environmental Sustainability, 8(Oct. 2014), 39–43.

    Article  Google Scholar 

  • Castella, J.-C., Jobard, E., Lestrelin, G., Nanthavong, K., & Lienhard, P. (2012). Maize expansion in Xieng Khouang province, Laos: what prospects for conservation agriculture? Paper presented at the The 3rd International Conference on Conservation Agriculture in Southeast Asia, Hanoi, 10-15/12/2012

  • Daniel, A. M., Persson, L., & Sandorf, E. D. (2018). Accounting for elimination-by-aspects strategies and demand management in electricity contract choice. Energy Economics, 73(2018), 80–90.

    Article  Google Scholar 

  • Duke, J. M., Borchers, A. M., Johnston, R. J., & Absetz, S. (2012). Sustainable agricultural management contracts: using choice experiments to estimate the benefits of land preservation and conservation practices. Ecological Economics, 74(Feb. 2012), 95–103.

    Article  Google Scholar 

  • EFICAS. (2017). Agrarian and land use transitions in maize production areas of Sayaboury and Xieng Khouang Provinces. EFICAS Annual Workshop. Luang Prabang: EFICAS Project.

  • Erdem, S., Campbell, D., & Thompson, C. (2014). Elimination and selection by aspects in health choice experiments: prioritising health service innovations. Journal of Health Economics, 38(Dec 2014), 10–22.

    Article  Google Scholar 

  • Fang, Y., & Wang, J. (2012). Selection of the number of clusters via the bootstrap method. Computational Statistics & Data Analysis, 56(3), 468–477.

    Article  Google Scholar 

  • Feder, G., Just, R. E., & Zilberman, D. (1985). Adoption of agricultural innovations in developing countries: a survey. Economic Development and Cultural Change, 33(25), 255–298.

    Article  Google Scholar 

  • Garnett, T., Appleby, M. C., Balmford, A., Bateman, I. J., Benton, T. G., Bloomer, P., Burlingame, B., Dawkins, M., Dolan, L., Fraser, D., Herrero, M., Hoffmann, I., Smith, P., Thornton, P. K., Toulmin, C., Vermeulen, S. J., & Godfray, H. C. J. (2013). Sustainable intensification in agriculture: premises and policies. Science, 341(6141), 33–34.

    Article  Google Scholar 

  • Greene, W. H. (2016). NLOGIT version 6: reference guide. New York: Econometric Software, Inc.

  • Greene, W. H., & Hensher, D. A. (2003). A latent class model for discrete choice analysis: contrasts with mixed logit. Transportation Research Part B: Methodological, 37(8), 681–698.

    Article  Google Scholar 

  • Hennig, C. (2010). FPC: Flexible Procedures for Clustering, available at

  • Hensher, D. (2014). Attribute processing as a behavioural strategy in choice making. In S. Hess & A. Daly (Eds.), Handbook of choice modelling (pp. 268–289). Cheltenham: Edward Elgar Publishing.

  • Hensher, D. A., Rose, J. M., & Greene, W. H. (2015). Applied choice analysis. Cambridge, U.K.: Cambridge University Press.

    Book  Google Scholar 

  • Hess, S., Stathopoulos, A., & Daly, A. (2012). Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies. Transportation, 39(3), 565–591.

    Article  Google Scholar 

  • Hijioka, Y., Lin, E., Pereira, J., Corlett, R., Cui, X., Insarov, G., Surjan, A., Field, C., Barros, V., & Mach, K. (2014). Asia climate change 2014: impacts, adaptation, and vulnerability, IPCC working group II contribution to AR5 (pp. 1327–1370). Cambridge UK and New York, USA: Cambridge: U. Press.

    Google Scholar 

  • Jaeck, M., & Lifran, R. (2014). Farmers’ preferences for production practices: a choice experiment study in the Rhone River Delta. Journal of Agricultural Economics, 65(1), 112–130.

    Article  Google Scholar 

  • Johnston, R. J., Boyle, K. J., Adamowicz, W., Bennett, J., Brouwer, R., Cameron, T. A., Hanemann, W. M., Hanley, N., Ryan, M., & Scarpa, R. (2017). Contemporary guidance for stated preference studies. Journal of the Association of Environmental and Resource Economists, 4(2), 319–405.

    Article  Google Scholar 

  • Jourdain, D., Boere, E., van den Berg, M., Dang, Q. D., Cu, T. P., Affholder, F., & Pandey, S. (2014). Water for forests to restore environmental services and alleviate poverty in Vietnam: a farm modeling approach to analyze alternative PES programs. Land Use Policy, 41(2014), 423–437.

    Article  Google Scholar 

  • Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision making under risk. Econometrica, 47(2), 263–291.

    Article  Google Scholar 

  • Kahneman, D., & Tversky, A. (2013). Prospect theory: an analysis of decision under risk. In L. C. MacLean & W. T. Ziemba (Eds.), Handbook of the fundamentals of financial decision making: Part II (pp. 99–127). Singapore: World Scientific.

    Chapter  Google Scholar 

  • Knight, F. H. (1921). Risk, uncertainty and profit. Boston: Houghton Mifflin Co..

    Google Scholar 

  • Knowler, D. (2015). Farmer adoption of conservation agriculture: a review and update. In M. Farooq & K. H. M. Siddique (Eds.), Conservation agriculture (pp. 621–642). Cham: Springer International Publishing.

    Google Scholar 

  • Knowler, D., & Bradshaw, B. (2007). Farmers’ adoption of conservation agriculture: a review and synthesis of recent research. Food Policy, 32(1), 25–48.

    Article  Google Scholar 

  • Lairez, J. (2018). Integrated assessment of cropping systems sustainability considering the rapid dynamics of farms. Hard data cited in a PhD Manuscript in preparation.

  • Läpple, D., Holloway, G., Lacombe, D. J., & O’Donoghue, C. (2017). Sustainable technology adoption: a spatial analysis of the Irish Dairy Sector. European Review of Agricultural Economics, 44(5), 810–835.

    Article  Google Scholar 

  • Leong, W., & Hensher, D. A. (2012). Embedding decision heuristics in discrete choice models: a review. Transport Reviews, 32(3), 313–331.

    Article  Google Scholar 

  • McFadden, D. (1974). The measurement of urban travel demand. Journal of Public Economics, 3(4), 303–328.

    Article  Google Scholar 

  • von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton (USA): Princeton University Press.

    Google Scholar 

  • Ortega, D. L., Waldman, K. B., Richardson, R. B., Clay, D. C., & Snapp, S. (2016). Sustainable intensification and farmer preferences for crop system attributes: evidence from Malawi’s central and southern regions. World Development, 87(2016), 139–151.

    Article  Google Scholar 

  • Pagiola, S. (1993). Soil conservation and the sustainability of agricultural production (p. 199). Stanford: Stanford University, Food Research Institute, Ph.D..

    Google Scholar 

  • Roumasset, J. A., Boussard, J.-M., & Singh, I. (Eds.). (1979). Risk, uncertainty and agricultural development. Laguna: Southeast Asian Regional Center for Graduate Study and Research in Agriculture and Agricultural Development Council.

  • Roy, A. D. (1952). Safety first and the holding of assets. Econometrica, 20(3), 431–449.

    Article  Google Scholar 

  • Scarpa, R., Ferrini, S., & Willis, K. (2005). Performance of error component models for status-quo effects in choice experiments. In R. Scarpa & A. Alberini (Eds.), Applications of simulation methods in environmental and resource economics (pp. 247–273). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Shackle, G. L. S. (1961). Decision, order, and time in human affairs. Cambridge: Cambridge University Press.

  • Tversky, A. (1972). Elimination by aspects: a theory of choice. Psychological Review, 79(4), 281–299.

    Article  Google Scholar 

  • Useche, P., Barham, B. L., & Foltz, J. D. (2013). Trait-based adoption models using ex-ante and ex-post approaches. American Journal of Agricultural Economics, 95(2), 332–338.

    Article  Google Scholar 

  • Valentin, C., Agus, F., Alamban, R., Boosaner, A., Bricquet, J. P., Chaplot, V., de Guzman, T., de Rouw, A., Janeau, J. L., Orange, D., Phachomphonh, K., Do Duy, P., Podwojewski, P., Ribolzi, O., Silvera, N., Subagyono, K., Thiébaux, J. P., Tran Duc, T., & Vadari, T. (2008). Runoff and sediment losses from 27 upland catchments in Southeast Asia: Impact of rapid land use changes and conservation practices. Agriculture, Ecosystems & Environment, 128 (4).225-238.

  • Van Loo, E. J., Caputo, V., Nayga, R. M., & Verbeke, W. (2014). Consumers’ valuation of sustainability labels on meat. Food Policy, 49(2014), 137–150.

    Google Scholar 

  • Vongvisouk, T., Broegaard, R. B., Mertz, O., & Thongmanivong, S. (2016). Rush for cash crops and forest protection: neither land sparing nor land sharing. Land Use Policy, 55(2016), 182–192.

    Article  Google Scholar 

  • Ward, P. S., Ortega, D. L., Spielman, D. J., & Singh, V. (2014). Heterogeneous demand for drought-tolerant rice: evidence from Bihar, India. World Development, 64(2014), 125–139.

    Article  Google Scholar 

  • Yiridoe, E. K., Langyintuo, A. S., & Dogbe, W. (2006). Economics of the impact of alternative rice cropping systems on subsistence farming: whole-farm analysis in northern Ghana. Agricultural Systems, 91(1–2), 102–121.

    Article  Google Scholar 

Download references


This work was supported by the Directorate-General for Development and Cooperation—EuropeAid (EuropeAid/132-657/L/ACT/LA) and the Agence Française de Développement (Conservation Agriculture within the Northern Upland Development Program, NUDP).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Damien Jourdain.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material


(DOCX 112 kb)


(DOCX 16 kb)


(DOCX 89 kb)


(DOCX 17 kb)


(DOCX 196 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jourdain, D., Lairez, J., Striffler, B. et al. Farmers’ preference for cropping systems and the development of sustainable intensification: a choice experiment approach. Rev Agric Food Environ Stud 101, 417–437 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: