This study examines how agricultural digitalisation affects agroecological efficiency, with the aim of informing the transition towards low-carbon, high-quality agricultural development. Using provincial panel data from China (2007-2022), we employ fixed-effects, spatial Durbin, and mediation models for empirical analysis. The results reveal three main findings: (i) The impact of agricultural digitalisation on agroecological efficiency presents a productivity paradox, characterised by a U-shaped relationship of initial suppression followed by promotion, a finding that remains robust after endogeneity is addressed and sensitivity checks are conducted. (ii) Agricultural digitalisation generates significant positive spatial spillover effects on neighbouring regions' agroecological efficiency. However, the realisation of these spillovers is contingent on the alignment between diffused technologies and local agricultural infrastructure. (iii) Farmland-scale management serves as a critical mediating channel. Furthermore, agricultural organisation - through stabilising profit expectations for scaled operators and facilitating land transfer through reduced tenure disputes - positively moderates the effective integration of digital technologies into agricultural practices. The findings suggest that digitally empowered sustainability goes beyond technological determinism. We recommend improving rural digital infrastructure, promoting land transfer, and reinforcing village-level collective economies to enhance agricultural organisation. Although it is geographically focused on China, this study offers theoretical and policy insights for advancing sustainable agriculture through digitalisation.
This study develops a theoretical analytical framework to examine the impact of digital finance on agricultural industrialisation and investigates the effects of both digital finance and agricultural industrialisation on inclusive economic growth, utilising interprovincial panel data from 2011 to 2020. The findings indicate that digital finance is conducive to economic inclusion and can enhance economic inclusion through the promotion of agricultural industrialisation. Furthermore, agricultural industrialisation serves as a vital conduit through which digital finance influences inclusive economic growth. This study highlights that the inclusive growth effect of digital finance escalates in conjunction with inclusive economic growth. By fostering the development of digital finance, this study advances agricultural industrialisation and contributes to the enhancement of inclusive economic growth, thereby offering policy insights to promote such growth.
Meat trade plays a crucial role in global food systems and is a key area of competition among nations. This study analyses the competitive network of global categorised meat trade using weighted network analysis, focusing on four major meat types. The results indicate that meat trade competition is dominated by large consuming nations such as China, Japan, and Germany due to their high demand. Beef, pork, and poultry markets exhibit relatively concentrated competition among a small number of high-strength countries, whereas the sheep market is characterised by a more balanced competitive structure and greater variability in the strength-degree relationship. Weighted clustering coefficients decline as degree increases, particularly in the sheep market, indicating looser competitive groupings. The topological average nearest neighbour degree exhibits significant disassortativity across all four meat categories, whereas the weighted measure remains relatively stable, indicating the persistence of weighted assortative characteristics. Overall, the findings reveal pronounced structural heterogeneity in global meat import competition in 2022 and provide useful implications for product-specific trade strategies and risk management.
This paper analyses the economic viability of Czech farms between 2015-2023 and examines the non-linear relationship between farm size and viability. Farm viability is measured using the Farm Economic Viability (FEV) indicator, adapted from the Modified Farm Net Value Added approach to account for constraints in accounting-based data. The analysis covers 1 443-2 211 farms annually, representing 44-65% of the utilised agricultural land area (UAA) in Czechia. Farms are classified by both UAA and by economic size. The results show that the share of economically viable farms ranged from 16.6% to 46.9%, with substantial year-to-year variability driven by market and policy conditions. Farms up to 150 ha exhibit higher average FEV values, whereas farms above 900 ha show below-average viability. In contrast, classification by economic size indicates higher viability among large farms, while small and medium-sized farms remain the most vulnerable. The predictive ability of the IN99 model is assessed using ROC (receiver operating characteristic) analysis; after modification, the optimised model achieves an AUC (area under the ROC curve) of 0.90 and a hit ratio of 83.3%. The findings confirm that farm size effects on viability depend critically on the chosen size definition and that accounting-based financial ratios can reliably predict farm economic viability.
Inner Mongolia operates using integrated agropastoral systems comprising crop and livestock production subsystems. To evaluate the efficiency of these interconnected subsystems, we develop an innovative parallel-bounded adjusted measure (BAM) model with shared inputs and outputs. Unlike conventional data envelopment analysis (DEA) models, this approach explicitly accounts for parallel structures with shared inputs and outputs, allowing for a more precise and nuanced assessment of efficiency. Our results are as follows: (i) regional efficiency disparities: Hohhot City, Ordos City, and Wuhai City are efficient, whereas Ulanqab City performs the worst; (ii) subsystem performance: crop production subsystems generally outperform livestock production subsystems; (iii) inefficiencies: we observe significant input slacks in cultivated land area, total agricultural machinery power, and initial livestock numbers, whereas the disposable income of rural residents requires significant improvement.
