What drives low-carbon agriculture? The experience of farms from the Wielkopolska region in Poland

Abstract

Because of global environmental problems, low-carbon agriculture has gained increasing importance both in developed and developing countries. Hence, there is a need to find ways to develop more efficient agricultural systems. The purpose of this article is to identify the drivers of low-carbon agriculture on farms in the Wielkopolska region (in Poland). We aimed to take an original approach to investigate low-carbon agriculture with a unique set of different economic and environmental variables and contribute to the literature, which is not very extensive in terms of microeconomic research, including research on farmers in the Wielkopolska region. Therefore, we employed a multiple-factor measurement model for structural equation modeling (SEM) of data collected individually from 120 farms in 2020. As a result, we formulated the following conclusions: the increasing productivity of factors (land, labor, and capital) have a positive effect on low-carbon farming, just as increasing fertilizer and energy efficiency. Moreover, thermal insulation is also important for low-carbon agriculture, with efficiency of fertilizer use being the most important factor. We believe that the issues of farm use of fertilizers and thermal insulation of buildings should be more broadly included in energy policy, both at the national and the European Union (EU) levels. Some of these factors however are already present in the common agricultural policy (CAP) for 2021-2027.

Keywords: Agricultural efficiency; Energy; Fertilizers; Low-carbon agriculture; Productivity; Structural equation modeling (SEM); The Wielkopolska region.

Fig. 1

Drivers of low-carbon farming in the Wielkopolska region: Evidence based on structural equation modeling using a multiple-factor measurement model with latent variables. Variables in blue ovals (“low_carbon” and “productivity”) are unobserved exogenous latent variables. Variables in rectangles are observed endogenous variables. Moreover, variables in wider rectangles are reference variables (“fert_u_eff” for construct “low_carbon” and “lab_prod” for construct “productivity”). ε (in small circles) are errors. The values in the rectangles for observed variables are standardized intercepts. The values on the black arrows between variables are standardized path coefficients (the first column “Coefficient” in table with detailed results). The values on green bidirectional arrows between two observed variables (between errors) or between observed variables and construct are standardized covariances, which are correlation coefficients (the column “Coefficient” in table with detailed results). All values presented in the figure are standardized values in standard deviation units. All variables and covariances are statistically significant (α = 0.05)