This study sought to investigate Chinese farmers' attitude towards animal welfare by using the Theory of Planned Behaviour (TPB). According to the TPB, an individual's intention to behave in a certain way is determined by his/her attitude towards the behaviour (specific attitude — importance — and general attitudes), the perceived behavioural control (easiness), and the supposed opinion of the people who are important to him/her (subjective norms). A total of 253 questionnaires are used, which included the three main animal productions in chena (swine, poultry and cattle). Chinese farmers have perceived the improvement of animal welfare as two abstracts: general attitudes (reward-seeking, and empathic farmer); and four specific categories of actions (favourable environment, animal health, humane treatment of animals and farmers' well-being). Our analysis revealted that general and specific attitudes were the strongest predictors of farmers' intentions to improve animal welfare in the questionnaire study. In fact, Chinese farmers considered it fairly important to improve the animal welfare meaures considered in the survey. In contrast, the same animal welfare measures were considered difficult to improve by the farmers as indicated by the lack of association between the easiness of improving animal welfare and the intentions. In addition, veterinarians, agricultural advisers, and scientific experts were considered to be relatively influential subjective norms as regards the activities of the farmers. This is the first study to provide an insight into the underlying meanings and values of Chinese farmers views on improvements to animal welfare.

No abstract

No abstract

Kernel moisture content at the harvest stage (KMC) is an important trait that affects the mechanical harvesting of maize grain, and the identification of genetic loci for KMC is beneficial for maize molecular breeding. In this study, we performed a multi-locus genome-wide association study (ML-GWAS) to identify quantitative trait nucleotides (QTNs) for KMC using an association mapping panel of 251 maize inbred lines that were genotyped with an Affymetrix CGMB56K SNP Array and phenotypically evaluated in three environments. Ninety-eight QTNs for KMC were detected using six ML-GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, PLARmEB, PKWmEB, and ISIS EM-BLASSO). Eleven of these QTNs were considered to be stable, as they were detected by at least four ML-GWAS models under a uniformed environment or in at least two environments and BLUP using the same ML-GWAS model. With qKMC5.6 removed, the remaining 10 stable QTNs explained <10% of the phenotypic variation, suggesting that KMC is mainly controlled by multiple minor-effect genetic loci. A total of 63 candidate genes were predicted from the 11 stable QTNs, and 10 candidate genes were highly expressed in the kernel at different time points after pollination. High prediction accuracy was achieved when the KMC-associated QTNs were included as fixed effects in genomic selection, and the best strategy was to integrate all KMC QTNs identified by all six ML-GWAS models. These results further our understanding of the genetic architecture of KMC and highlight the potential of genomic selection for KMC in maize breeding.