Agriculture

Fusarium graminaerum causes Fusarium Head Blight (FHB) on wheat, reduces yield, and contaminates food and feed. It is therefore of paramount importance to control its growth or convert its harmful mycotoxins. This study aimed to find yeasts with biocontrol activity against F. graminearum, and to identify genes with potential detoxifying activities, using microbiological, molecular methods and bioinformatics. Co-cultivation tests showed that Schizosaccharomyces japonicus was able to inhibit the growth of F. graminearum. Transcriptomic analysis of the yeast cells co-cultured with F. graminearum highlighted differentially expressed genes (DEGs) encoding various enzymes, such as oxidoreductases, transferases, hydrolases, or genes involved in transmembrane transport. Three trichothecene-3-O-acetyltransferase homologous genes, which can convert trichothecenes to less toxic forms, were also among them. A database search showed that several yeast species contained this gene, including S. japonicus, which unexpectedly had seven copies. Real-time PCR analysis and mycotoxin tolerance tests confirmed that some of these genes could be induced by deoxynivalenol (DON), and S. japonicus had stronger DON tolerance than the related S. pombe, whose genome did not contain such a gene. This study is the first to report the biocontrol efficacy of S. japonicus against F. graminearum and the identification of its potential detoxification genes, offering promising new avenues for biotechnological applications in food safety.

Agricultural competitiveness across European Union Member States exhibits persistent disparities that cannot be fully explained by technology, climate exposure or institutional quality in isolation. This study examines whether functional fragmentation—defined as the cumulative simultaneity of biological, technological, managerial and institutional production functions—constitutes a structural determinant of competitiveness over the period 2004–2023. Using harmonized country-level data from FAOSTAT, FADN, WDI, WGI and WMO, we construct a composite competitiveness index and a multiplicative fragmentation index and estimate two-way fixed-effects panel models. Functional fragmentation is negatively associated with competitiveness (β = −3.734, p < 0.01). A 10% reduction in fragmentation (ΔFF = −0.042) increases competitiveness by approximately 0.16 index units, corresponding to about 16% of one standard deviation. The interquartile fragmentation gap (ΔFF ≈ 0.18) implies a competitiveness difference of 0.67 units, nearly two-thirds of one standard deviation, indicating economically substantial structural effects. These results indicate that fragmentation primarily shifts the baseline level of performance rather than altering marginal responses to technological intensity or climate shocks. The findings identify functional fragmentation as a structural coordination constraint within EU agriculture and highlight the importance of systemic coherence alongside technological upgrading in competitiveness-oriented policy design.

Climate variability represents a growing challenge for livestock systems; however, its indirect economic effects remain insufficiently understood, particularly in data-scarce contexts. This study evaluates whether satellite-derived bioclimatic indices propagate into short-term variability of livestock-related sales from a digital agriculture perspective. Weekly commercial records from two geographically proximate livestock branches in Ecuador were integrated with meteorological data provided from NASA POWER to compute the Temperature Humidity Index (THI). A basal temperature index, defined as a four-week moving average of THI, and a corresponding thermal anomaly were derived in order to represent both cumulative and short-term thermal conditions. Linear time series models incorporating exogenous variables (ARIMAX) and a non-linear machine learning approach (Random Forest) were employed using lagged climatic and economic features. The results showed that linear models had limited explanatory capacity, indicating that short-term sales variability was primarily driven by market dynamics and logistical processes rather than direct climatic forcing. While the Random Forest model achieved better predictive performance, this was mainly due to its ability to capture systemic inertia and autoregressive structure in the sales series; climatic variables only provided a secondary, indirect signal. These findings highlight the value of artificial intelligence in identifying weak and delayed climate-related patterns in aggregated commercial indicators and support of satellite-based climate data in market-level decision making in livestock supply chains where animal-level measurements are unavailable.