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Zarraonaindia, I.

Publications and source records attributed to Zarraonaindia, I..

2 recordsLinked to original sources

Biological nitrification inhibition (BNI) in wheat for climate adaptation in acidic and alkaline soils

Anthropogenic disturbances to nitrogen (N) cycling, particularly through agricultural N use, have intensified nitrous oxide (N2O) emissions. Developing wheat lines with biological nitrification inhibition (BNI) is a promising strategy to reduce such emissions. However, the effectiveness of BNI depends not only on plant characteristics, but also on how they interact with environmental factors such as soil pH and elevated atmospheric CO2 (eCO2). This study evaluated the response of a BNI-capable wheat line (Triticum aestivum cv. ROELFS) to eCO2 under contrasting soil pH during the 30 days following ammonium fertilization. Two wheat lines (ROELFS-Control and ROELFS-BNI) were grown under ambient (400 ppm) and elevated (800 ppm) CO2 in acidic (pH 5.3) and alkaline (pH 8.8) soils. N2O emissions, nitrifying and denitrifying microbial communities, and soil chemical properties were monitored to assess plant-soil-microbe interactions. ROELFS-BNI consistently reduced N2O emissions under all conditions by reducing archaeal (Nitrososphaeraceae) and bacterial nitrifiers (Nitrosomonadaceae, Nitrospiraceae) without major shifts in overall microbial composition, indicating high specificity of BNI exudates. Elevated CO2 effects on N2O emissions were pH-dependent. In acidic soil, eCO2 increased emissions in ROELFS-Control but not in ROELFS-BNI, likely due to the enrichment of complete denitrifiers (e.g. Rhodanobacteraceae). However, in alkaline soil, eCO2 reduced N2O emissions in both Control and BNI lines, especially the latter, which was associated with a higher abundance of N2O-reducing denitrifiers (e.g. Burkholderiaceae). This study highlights the potential of ROELFS-BNI wheat as a sustainable practice to mitigate N pollution adaptable to diverse soils and predicted CO2 atmospheric conditions.

plant biology↗

A SNP-based honey bee paternity assignment test for evaluating the effectiveness of mating stations and its application to the Ataun valley, Basque Country, Spain

Geographically isolated mating stations are deployed across Europe to facilitate controlled mating with selected drone-producing colonies. To assess the reliability of these stations, we developed a paternity assignment test using a custom Illumina genotyping chip with 6457 SNPs based on two metrics (number of mismatch alleles and kinship). The method demonstrated remarkable accuracy during validation with an independent dataset of known parent-offspring pairs, with an accuracy rate of 97.7%. We then applied the developed paternity assignment test to the Apis mellifera iberiensis mating station in the Ataun valley, Basque Country, Spain, in 2021. Drone-producing colonies in the valley were sampled and genotyped, as well as 156 worker offspring of queens mated at the station, and 56 drones collected in the drone congregation area. Out of the 156 worker samples, we could assign paternity of 120 (76.9%) to one of the drone-producing colonies in the valley, while 23.1% were of unknown patriline. Out of the 56 drones collected in the air, 52 (92.9%) were assigned to drone-producing colonies. We were also able to determine the colonies and apiaries that made the most significant contributions to the matings. This information aids in effective apiary management, including the selection of suitable mating station locations and the positioning of drone-producing colonies therein. Overall, our SNP-based paternity assignment test offers a valuable tool for evaluating mating station effectiveness across Europe, crucial for advancing breeding objectives in honey bee populations.

genetics↗