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The How does extended grassland-based dairy production affect energy productivity and GHG emission intensity? Trade-offs in milk-specialised farms

Łukasz Gonera
Poznan University of Economics and Business
Bazyli Czyżewski
Poznan University of Economics and Business
Jakub Staniszewski
Poznań University of Economics and Business

Published 2026-08-30

Keywords

  • dairy farming,
  • grassland share,
  • energy productivity,
  • GHG emission intensity,
  • generalised propensity score

How to Cite

Gonera, Łukasz, Czyżewski, B., & Staniszewski, J. (2026). The How does extended grassland-based dairy production affect energy productivity and GHG emission intensity? Trade-offs in milk-specialised farms. Bio-Based and Applied Economics. https://doi.org/10.36253/bae-20286

Funding data

Abstract

This study examines how the share of grassland shapes the relationship between energy productivity and greenhouse gas (GHG) emission intensity in Polish milk-specialised farms operating under mixed-forage systems. The analysis uses a balanced panel of dairy farms observed from 2014 to 2022 in the Polish FADN database. In the first stage, farm-level GHG estimates are used together with FADN input and output data to construct Hicks-Moorsteen indices of energy productivity change and GHG emission-intensity change. In the second stage, the causal effect of grassland share is identified using the Generalised Propensity Score combined with Generalised Additive Models. The results indicate that energy productivity generally increases with the grassland share, although the effect is non-linear and strongest at very low and very high levels. GHG emission intensity follows a U-shaped pattern: it declines as grassland share rises to around 50%, then increases at higher shares. The findings suggest a synergy zone at moderate grassland shares and a trade-off when pasture-based production becomes too dominant.

References

  1. Ang, F., Kerstens, K., & Sadeghi, J. (2023). Energy productivity and greenhouse gas emission intensity in Dutch dairy farms: A Hicks–Moorsteen by-production approach under non-convexity and convexity with equivalence results. Journal of Agricultural Economics, 74(2), 492–509. https://doi.org/10.1111/1477-9552.12511
  2. Baležentis, T., Butkus, M., & Štreimikienė, D. (2023). Energy productivity and GHG emission in the european agriculture: The club convergence approach. Journal of Environmental Management, 342, 118238–118238. https://doi.org/10.1016/j.jenvman.2023.118238
  3. Baráth, L., Fertő, I., & Bojnec, Š. (2018). Are farms in less favored areas less efficient? Agricultural Economics, 49(1), 3–12. https://doi.org/10.1111/agec.12391
  4. Bos, J. F. F. P., Smit, A. (Bert) L., & Schröder, J. J. (2013). Is agricultural intensification in The Netherlands running up to its limits? NJAS - Wageningen Journal of Life Sciences, 66, 65–73. https://doi.org/10.1016/j.njas.2013.06.001
  5. Briec, W., Kerstens, K., & Van de Woestyne, I. (2020). Nonconvexity in production and cost functions: An exploratory and selective review. In S. C. Ray, R. Chambers, & S. Kumbhakar (Eds.), Handbook of production economics (pp. 1–34). Springer. https://doi.org/10.1007/978-981-10-3450-3_15-1
  6. Cashman, O., Casey, I., & Humphreys, J. (2024). The economic performance of grassland-based milk production using best practices to lower greenhouse gas and ammonia emissions. Agricultural Systems, 221, 104105. https://doi.org/10.1016/j.agsy.2024.104105
  7. Czyżewski, B., Staniszewski, J., Staniszewska, J., & Guth, M. (2025). Does increasing agricultural efficiency contribute to food security? Trade-offs of value addition in crop production. Sustainable Development, 1–32. https://doi.org/10.1002/sd.70043
  8. Daraio, C., & Simar, L. (2007). Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications. Springer. https://doi.org/10.1007/978-0-387-35231-2
  9. European Commission. (2014). Profitability of permanent grassland: Starting paper. European Innovation Partnership for Agricultural Productivity and Sustainability (EIP-AGRI) Focus Group 9. https://ec.europa.eu/eip/agriculture/sites/ default/files/fg9_permanent_grassland_profitability_starting_paper_2014_en.pdf
  10. Fenger, F., Casey, I. A., Buckley, C., & Humphreys, J. (2023). Effects of grazing platform stocking rate on productivity and profitability of pasture-based dairying in a fragmented farm scenario. Journal of Dairy Science, 106(11), 7750–7768. https://doi.org/10.3168/jds.2023-23362
  11. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
  12. Gadanakis, Y., Bennett, R., Park, J., & Areal, F. J. (2015). Evaluating the Sustainable Intensification of arable farms. Journal of Environmental Management, 150, 288–298. https://doi.org/10.1016/j.jenvman.2014.10.005
  13. García-Souto, V., Foray, S., Lorenzana, R., Veiga-López, M., Pereira-Crespo, S., González-González, L., Flores-Calvete, G., Báez, D., Botana, A., & Resch-Zafra, C. (2022). Assessment of greenhouse gas emissions in dairy cows fed with five forage systems. Italian Journal of Animal Science, 21(1), 378–389. https://doi.org/10.1080/1828051x.2022.2036641
  14. Gerber, P., Vellinga, T., Opio, C., & Steinfeld, H. (2011). Productivity gains and greenhouse gas emissions intensity in dairy systems. Livestock Science, 139 (1-2), 100–108. https://doi.org/10.1016/j.livsci.2011.03.012
  15. Gislon, G., Ferrero, F., Bava, L., Borreani, G., Dal Prà, A., Pacchioli, M. T., Sandrucci, A., Zucali, M., & Tabacco, E. (2020). Forage systems and sustainability of milk production: Feed efficiency, environmental impacts and soil carbon stocks. Journal of Cleaner Production, 260, 121012. https://doi.org/10.1016/j.jclepro.2020.121012
  16. Guerci, M., Knudsen, M. T., Bava, L., Zucali, M., Schönbach, P., & Kristensen, T. (2013). Parameters affecting the environmental impact of a range of dairy farming systems in Denmark, Germany and Italy. Journal of Cleaner Production, 54, 133–141. https://doi.org/10.1016/j.jclepro.2013.04.035
  17. Guillen, M. D., Charles, V., & Aparicio, J. (2025). Estimating non-overfitted convex production technologies: A stochastic machine learning approach. European Journal of Operational Research, 323(1), 224–240. https://doi.org/10.1016/j.ejor.2024.11.030
  18. Hanrahan, L., McHugh, N., Hennessy, T., Moran, B., Kearney, R., Wallace, M., & Shalloo, L. (2018). Factors associated with profitability in pasture-based systems of milk production. Journal of Dairy Science, 101, 5474–5485. https://doi.org/10.3168/jds.2017-13223
  19. Hastie, T., & Tibshirani, R. (1990). Generalized Additive Models. London: Chapman & Hall.
  20. Hirano, K., & Imbens, G. W. (2004). The propensity score with continuous treatments. In A. Gelman & X. L. Meng (Eds.), Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (pp. 73–84). Wiley.
  21. Hou, J., Styles, D., Zhang, W. (2022) Improving nutrient and economic efficiency of dairy intensification depends on intensive use of scattered cropland, Sustainable Production and Consumption, 30, 454-466. https://doi.org/10.1016/j.spc.2021.12.027
  22. Huber, R., Le’Clec’h, S., Buchmann, N., & Finger, R. (2022). Economic value of three grassland ecosystem services when managed at the regional and farm scale. Scientific Reports, 12, 4194. https://doi.org/10.1038/s41598-022-08198-w
  23. Imai, K., & van Dyk, D. A. (2004). Causal inference with general treatment regimes: Generalizing the propensity score. Journal of the American Statistical Association, 99(467), 854–866. https://doi.org/10.1198/016214504000001187
  24. IPCC. (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme (S. Eggleston, L. Buendia, K. Miwa, T. Ngara, & K. Tanabe, Eds.). IGES.
  25. Jordon, M. W., Buffet, J.-C., Dungait, J. A. J., Galdos, M. V., Garnett, T., Lee, M. R. F., Lynch, J., Röös, E., Searchinger, T. D., Smith, P., & Godfray, H. C. J. (2024). A restatement of the natural science evidence base concerning grassland management, grazing livestock and soil carbon storage. Proceedings of the Royal Society B: Biological Sciences, 291(20232669). https://doi.org/10.1098/rspb.2023.2669
  26. Kankanamge, E. K., Ramilan, T., Tozer, P. R., de Klein, C., Romera, A., & Pieralli, S. (2025). Greenhouse gas mitigation in pasture-based dairy production systems in New Zealand: A review of mitigation options and their interactions. Climate Smart Agriculture, 100071. https://doi.org/10.1016/j.csag.2025.100071
  27. KOBiZE. (2024). Poland’s National Inventory Report 2024 .
  28. Kraatz, S. (2012). Energy intensity in livestock operations – Modeling of dairy farming systems in Germany. Agricultural Systems, 110, 90–106. https://doi.org/10.1016/j.agsy.2012.03.007
  29. Kuhn, L., Balezentis, T., Hou, L., & Wang, D. (2020). Technical and environmental efficiency of livestock farms in China: A slacks-based DEA approach. China Economic Review, 62. https://doi.org/10.1016/j.chieco.2018.08.009
  30. Latruffe, L., Niedermayr, A., Desjeux, Y., Dakpo, K. H., Ayouba, K., Schaller, L., Kantelhardt, J., Jin, Y., Kilcline, K., Ryan, M., & O’Donoghue, C. (2023). Identifying and assessing intensive and extensive technologies in European dairy farming. European Review of Agricultural Economics, 50(4), 1482–1519. https://doi.org/10.1093/erae/jbad023
  31. Liu, L., Sayer, E. J., Deng, M., Li, P., Liu, W., Wang, X., et al. (2023). The grassland cycle: Mechanisms, responses to global changes, and potential contribution to neutrality. Fundamental Research, 3, 209–218. https://doi.org/10.1016/j.fmre.2022.09.028
  32. Llanos, E., Astigarraga, L., & Picasso, V. (2018). Energy and economic efficiency in grazing dairy systems under alternative intensification strategies. European Journal of Agronomy, 92, 133–140. https://doi.org/10.1016/j.eja.2017.10.010
  33. Madau, F. A., Furesi, R., & Pulina, P. (2017). Technical efficiency and total factor productivity changes in European dairy farm sectors. Agricultural and Food Economics, 5, 17. https://doi.org/10.1186/s40100-017-0085-x
  34. Meunier, C., Ryschawy, J., & Martin, G. (2025). Reintegrating livestock onto crop farms: A step towards agro-environmental sustainability? Agricultural Systems, 227, 104356. https://doi.org/10.1016/j.agsy.2025.104356
  35. Moerkerken, A., Duijndam, S., Blasch, J., van Beukering, P., & Smit, A. (2021). Determinants of energy efficiency in the Dutch dairy sector: dilemmas for sustainability. Journal of Cleaner Production, 293, 126095. https://doi.org/10.1016/j.jclepro.2021.126095
  36. Mohsenimanesh, A., LeRiche, E. L., Gordon, R., Clarke, S., MacDonald, R. D., MacKinnon, I., & VanderZaag, A. C. (2021). Review: Dairy farm electricity use, conservation, and renewable production—A global perspective. Applied Engineering in Agriculture, 37(5), 977–990. https://doi.org/10.13031/aea.14621
  37. Moutinho, V., Robaina, M., & Macedo, P. B. (2018). Economic-environmental efficiency of European agriculture – a generalized maximum entropy approach. Zemědělská Ekonomika, 64(No. 10), 423–435. https://doi.org/10.17221/45/2017-agricecon
  38. Mrówczyńska-Kamińska, A., Bajan, B., Pawłowski, K. P., Genstwa, N., & Zmyślona, J. (2021). Greenhouse gas emissions intensity of food production systems and its determinants. PLOS ONE, 16(4), e0250995. https://doi.org/10.1371/journal.pone.0250995
  39. Myrgiotis, V., Smallman, T. L., & Williams, M. (2022). The carbon budget of the managed grasslands of Great Britain—Informed by Earth observations. Biogeosciences, 19, 4147–4170. https://doi.org/10.5194/bg-19-4147-2022
  40. Nilsson, F. O. L. (2009). Biodiversity on Swedish pastures: Estimating biodiversity production costs. Journal of Environmental Management, 90(1), 131–143. https://doi.org/10.1016/j.jenvman.2007.08.015
  41. Oenema, J., & Oenema, O. (2021). Intensification of grassland-based dairy production and its impacts on land, nitrogen, and phosphorus use efficiencies. Frontiers of Agricultural Science and Engineering, 8(1), 130–147. https://doi.org/10.15302/J-Fase-2020376
  42. Paris, B., Vandorou, F., Balafoutis, A. T., Vaiopoulos, K., Kyriakarakos, G., Manolakos, D., & Papadakis, G. (2022). Energy use in open-field agriculture in the EU: A critical review recommending energy efficiency measures and renewable energy sources adoption. Renewable and Sustainable Energy Reviews, 158, 112098. https://doi.org/10.1016/j.rser.2022.112098
  43. Pol-van Dasselaar, A. V. D., Bastiaansen-Aantjes, L., Bogue, F., O’Donovan, M., and Huyghe, C. (Eds.) (2019). Grassland use in Europe. éditions Quae Edn.
  44. Prandecki, K., & Wrzaszcz, W. (2025). Farm Greenhouse Gas Emissions as a Determinant of Sustainable Development in Agriculture—Methodological and Practical Approach. Sustainability, 17(14), 6452. https://doi.org/10.3390/su17146452
  45. Queiroz, C., Beilin, R., Folke, C., & Lindborg, R. (2014). Farmland abandonment: Threat or opportunity for biodiversity conservation? A global review. Frontiers in Ecology and the Environment, 12, 288–296. https://doi.org/10.1890/120348
  46. Resare Sahlin, K., Gordon, L. J., Lindborg, R., Piipponen, J., Van Rysselberge, P., Rouet-Leduc, J., & Röös, E. (2024). An exploration of biodiversity limits to grazing ruminant milk and meat production. Nature Sustainability, 7(9), 1160–1170. https://doi.org/10.1038/s41893-024-01398-4
  47. Schneider, U. A., & Smith, P. (2008). Energy intensities and greenhouse gas emission mitigation in global agriculture. Energy Efficiency, 2(2), 195–206. https://doi.org/10.1007/s12053-008-9035-5
  48. Shine, P., Upton, J., Sefeedpari, P., & Murphy, M. D. (2020). Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses. Energies, 13(5), 1288. https://doi.org/10.3390/en13051288
  49. Smolková, B., Blizkovsky, P., Lacina, L., Vavrina, J., Skladanka, J., Knot, P., Horky, P., & Hrabe, F. (2025). Carbon as the central economic factor in sustainable and profitable grassland management. Frontiers in Sustainable Food Systems, 9, 1579665. https://doi.org/10.3389/fsufs.2025.1579665
  50. Staniszewski, J., Czyżewski, B., & Matuszczak, A. (2025). Predictors of emission-adjusted efficiency in crop farms: Interactions and non-linearities within the nexus of social, economic and natural factors. Science of The Total Environment, 996, 180148. https://doi.org/10.1016/j.scitotenv.2025.180148
  51. Stępień, S., Czyżewski, B., Sapa, A., Borychowski, M., Poczta, W., & Poczta-Wajda, A. (2021). Environmental efficiency of small-scale farming in Poland and its institutional drivers. Journal of Cleaner Production, 279, 123721. https://doi.org/10.1016/j.jclepro.2020.123721
  52. Taube, F., Gierus, M., Hermann, A., Loges, R., & Schönbach, P. (2013). Grassland and globalization - challenges for north-west European grass and forage research. Grass and Forage Science, 69(1), 2–16. https://doi.org/10.1111/gfs.12043
  53. Upton, J., Humphreys, J., Groot Koerkamp, P. W. G., French, P., Dillon, P., & De Boer, I. J. M. (2013). Energy demand on dairy farms in Ireland. Journal of Dairy Science, 96(10), 6489–6498. https://doi.org/10.3168/jds.2013-6874
  54. Urak, F., Bilgic, A., Florkowski, W. J., & Bozma, G. (2024). Confluence of COVID-19 and the Russia-Ukraine conflict: Effects on agricultural commodity prices and food security. Borsa Istanbul Review. https://doi.org/10.1016/j.bir.2024.02.008
  55. Van Puyenbroeck, T. (2002). Profit efficiency analysis under limited information: With special reference to technical efficiency (Discussion Paper No. 02/02). Katholieke Universiteit Leuven, Department of Economics. Retrieved from https://feb.kuleuven.be/drc/Economics/research/dps-papers/dps02/dps0202.pdf
  56. Wood, S. N. (2017). Generalized Additive Models: An Introduction with R (2nd ed.). CRC Press. https://doi.org/10.1201/9781315370279
  57. Wrzaszcz W., (2014). Sustainability of agricultural holdings in Poland, Studia i monografie, No. 161, IERiGŻ PIB, Warszawa
  58. Zarrin, M., & Brunner, J. O. (2023). Analyzing the accuracy of variable returns to scale data envelopment analysis models. European Journal of Operational Research, 308(3), 1286–1301. https://doi.org/10.1016/j.ejor.2022.12.015
  59. Zhu, Y., Coffman, D. L., & Ghosh, D. (2015). A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments. Journal of Causal Inference, 3(1), 25–40. https://doi.org/10.1515/jci-2014-0022