Frederic Ang

Circular agriculture is vital to achieve a substantial reduction of greenhouse gas (GHG) emissions. Optimizing resources and land use are an essential circularity principle. The objective of this article is to assess the extent to which land optimization can simultaneously reduce GHG emissions and increase production on dairy farms. In addition, we explore the potential reduction of GHG emissions under four different pathways. The empirical application combines the network Data Envelopment Analysis (DEA) with the by‐production approach. This study focuses on a representative sample of Dutch dairy farms over the period of 2010–2019. Our results suggest that farms can simultaneously increase production and reduce GHG emissions by both 5.1%. However, only 0.6% can be attributed to land optimization. The land optimization results show that on average 25.3% of total farm size should be allocated to cropland, which is 6.7% more than the actual land allocation. GHG emissions could be reduced by 11.79% without changing the level of inputs and outputs. This can be achieved by catching up with the mitigation practices of the best performing peers.

Accessibility to financial resources is considered a prevalent problem in the agricultural sector. We develop an approach to quantify the long‐term opportunity costs of financial constraints in relation to peers who do not face any financial constraints. Using data on past financial performance, we assess creditworthiness and the size of an additional accessible bank loan to farmers. Combining this with data on reported expenditure, we determine the accessible finance. We quantify the opportunity cost as the forgone dynamic profit (intertemporal profit in current‐value terms) from financial constraints. Using data envelopment analysis, we apply our approach to 264 specialised Dutch dairy farms for the years 2006–2017 and explore the potential impact of changes in finance provision for several scenarios. Our results show an increasing gap between frontrunners and other farmers, as the latter generate progressively less dynamic profit in comparison to their best peers. The gap between the dynamic profit of the average farm and that of its best peers from their production and investment decisions made over the span of 1 year grew from €40,040 in 2009 to €114,548 in 2017. However, the growth is not driven by insufficient access to finance. Financial constraints can only explain 6% of the forgone dynamic profit in 2009 and as little as 1% for 2017. The number of farms classified as financially constrained in comparison to their peers decreases in our sample from 44% in 2009 to 8% in 2017. This suggests that non‐financial factors are driving the growing gap.