White lupin is a high-protein crop requiring drought tolerance improvement. This study focused on a genetically-broad population of 138 lines to investigate the phenotypic variation and genotype × environment interaction (GEI) for grain yield and other traits across drought-prone and moisture-favourable managed environments, the trait genetic architecture and relevant genomic regions by a GWAS using 9828 mapped SNP markers, and the predictive ability of genomic selection (GS) models. Water treatments across two late cropping months implied max. available soil water content of 60–80% for favourable conditions and from wilting point to 15% for severe drought. Line yield responses across environments featured a genetic correlation of 0.84. Relatively better line yield under drought was associated with an increased harvest index. Two significant QTLs emerged for yield in each condition that differed across conditions. Line yield under stress displayed an inverse linear relationship with the onset of flowering, confirmed genomically by a common major QTL. An adjusted grain yield computed as deviation from phenology-predicted yield acted as an indicator of intrinsic drought tolerance. On the whole, the yield in both conditions and the adjusted yield were polygenic, heritable, and exploitable by GS with a high predictive ability (0.62–0.78). Our results can support selection for climatically different drought-prone regions.

White lupin is a promising high-protein crop, the cultivation of which is limited by a lack of adaptation to soils that are even just mildly calcareous. This study aimed to assess the phenotypic variation, the trait architecture based on a GWAS, and the predictive ability of genome-enabled models for grain yield and contributing traits of a genetically-broad population of 140 lines grown in an autumn-sown environment of Greece (Larissa) and a spring-sown environment of the Netherlands (Ens) that featured moderately calcareous and alkaline soils. We found large genotype × environment interaction and modest or nil genetic correlation for line responses across locations for grain yield, a lime susceptibility score, and other traits, with the exception of individual seed weight and plant height. The GWAS identified significant SNP markers associated with various traits that were markedly inconsistent across locations, while providing direct or indirect evidence for widespread polygenic trait control. Genomic selection proved to be a feasible strategy, owing to a moderate predictive ability for yield and lime susceptibility in Larissa (the site featuring greater lime soil stress). Other supporting results for breeding programs where the identification of a candidate gene for lime tolerance and the high reliability of genome-enabled predictions for individual seed weight.

Enhanced pea cultivation, which can increase the sustainability of European agriculture, requires better-performing cultivars. This study investigated the genomic selection (GS) ability to predict grain yield, protein content, and protein yield on the same or a different genetic base (target/non-target GB) relative to that employed for model training. GS models were developed on 276 lines from three Recombinant Inbred Line (RIL) populations evaluated in three Italian autumn-sown environments using 5537 SNPs from genotyping by sequencing. Validation in two cropping years concerned 108 independent lines from five RIL populations, of which two belonged to the GS training set, and three shared one parent each with training populations. A genome-wide association study performed on the GS training set using 18,674 SNPs highlighted the polygenic control of protein content and grain yield, with several environment-dependent QTLs for yield. Intermediate/high predictive ability within or across populations emerged for all traits in the target GB (0.359–0.675), with some variation depending on the population. Predictive ability in the non-target GB was modest/intermediate for protein content, and null/poor for the other traits. No inverse correlation emerged between grain yield and protein content. GS proved useful for all traits in the target GB and for protein content in a non-target GB.

Aim of study: Efficient recycling of digestates in livestock systems is a way for maintaining satisfactory crop yields and reducing environmental pollution. Substitution of mineral N fertilisers with digestates was demonstrated under field conditions for silage maize (Zea mays L.), while studies evaluating digestate’s fertiliser efficacy for legume crops relevant for the livestock sector, namely alfalfa (Medicago sativa L.) and soybean (Glycine max (L.) Merr.), remain limited. This study evaluated digestate’s efficacy in enhancing crop yield of silage maize, alfalfa forage and soybean grain under irrigated field conditions in northern Italy. Area of study: Mediterranean North climate (Lombardy Region, northern Italy). Material and methods: During the cropping seasons 2020 to 2022, field experiments were conducted involving the application of digestates, mineral fertilisers, and digestates enriched with microbial-based biostimulants. The effect of each treatment was evaluated considering two varieties of each crop, measuring the yield of maize silage, alfalfa forage and soybean grain. Main results: Digestates providing 250 kg N ha–1 to maize, and 50 kg N ha–1 to soybean resulted in equal yield compared to mineral fertiliser. The response of the three crops to fertilisation treatments was consistent between years and varieties. The lack of response of alfalfa to digestate did not support the application of digestates to this crop. Finally, the addition of biostimulant increased maize yield in only one location, requiring further evaluation under field conditions. Conclusions: Liquid digestate can effectively substitute urea in maize fertilization, is beneficial for soybean at low application rates, but is not recommended for alfalfa under high-fertility conditions.