Measuring the Economic Performance of Small Ruminant Farms Using Balanced Scorecard and Importance-Performance Analysis: A European Case Study

Milk transcriptome biomarker identification to enhance feed efficiency and reduce nutritional costs in dairy ewes

In recent years, rising prices for high-quality protein-based feeds have significantly increased nutrition costs. Consequently, investigating strategies to reduce these expenses and improve feed efficiency (FE) have become increasingly important for the dairy sheep industry. This research investigates the impact of nutritional protein restriction (NPR) during prepuberty and FE on the milk transcriptome of dairy Assaf ewes (sampled during the first lactation). To this end, we first compared transcriptomic differences between NPR and control ewes. Subsequently, we evaluated gene expression differences between ewes with divergent FE, using feed conversion ratio (FCR), residual feed intake (RFI), and consensus classifications of high- and low-FE animals for both indices. Lastly, we assess milk gene expression as a predictor of FE phenotype using random forest. No effect was found for the prepubertal NPR on milk performance or FE. Moreover, at the milk transcriptome level, only one gene, HBB, was differentially expressed between the NPR (n = 14) and the control group (n = 14). Further, the transcriptomic analysis between divergent FE sheep revealed 114 differentially expressed genes (DEGs) for RFI index (high-FERFI = 10 vs low-FERFI = 10), 244 for FCR (high-FEFCR = 10 vs low-FEFCR = 10), and 1 016 DEGs between divergent consensus ewes for both indices (high-FEconsensus = 8 vs low-FEconsensus = 8). These results underscore the critical role of selected FE indices for RNA-Seq analyses, revealing that consensus divergent animals for both indices maximise differences in transcriptomic responses. Genes overexpressed in high-FEconsensus ewes were associated with milk production and mammary gland development, while low-FEconsensus genes were linked to higher metabolic expenditure for tissue organisation and repair. The best prediction accuracy for FE phenotype using random forest was obtained for a set of 44 genes consistently differentially expressed across lactations, with Spearman correlations of 0.37 and 0.22 for FCR and RFI, respectively. These findings provide insights into potential sustainability strategies for dairy sheep, highlighting the utility of transcriptomic markers as FE proxies.

Exploring innovation adoption behavior for sustainable development of Mediterranean tree crops

Introduction

The combination of knowledge, personal skills and company resources influences, all things being equal, such as the availability of new technologies, market conditions and other factors external to the company, farmers in their innovation choices. This study is an attempt to understand which psychological constructs influence the decision-making process of farmers specialized in typical Mediterranean crops with regard to innovation. Previous studies on the adoption of agricultural innovations have often considered socio economic characteristics and ignored the underlying motivational factors that influence the behavioral intention of farmers.

Methods

This study adopted three socio-psychological constructs, Attitude (ATT), Subjective Norm (SN), and Perceived Behavioral Control (PBC), derived from the Theory of Planned Behavior (TPB), and proposed three new constructs, Perceived Innovations Characteristics (PIC), Benefits (B), and Transferability (T), thus using an Extended Model of the Theory of Planned Behavior.

Results

The outcome of the multiple regression revealed that farmers' intention (I) to adopt sustainable irrigation innovations is positively influenced by attitude (ATT), subjective norm (SN), and perceived innovation characteristics (PIC). This last construct had mediating effects on the indirect relationships between PBC, benefits (B), transferability (T), and intention (I).

Discussion

The results provide numerous insights, useful both for outlining the demand for innovation and for calibrating future policies aimed at the primary sector, especially on the sustainable management of irrigation resources. In particular, the analyses carried out highlight the importance of factors external to the company as key levers in shaping the demand for innovations.

A Change Management Approach with the Support of the Balanced Scorecard and the Utilization of Artificial Neural Networks

Artificial Intelligence (AI) has revolutionized the way organizations face decision-making issues. One of these crucial elements is the implementation of organizational changes. There has been a wide-spread adoption of AI techniques in the private sector, whereas in the public sector their use has been recently extended. One of the greatest challenges that European governments have to face is the implementation of a wide variety of European Union (EU) funding programs which have evolved in the context of the EU long-term budget. In the current study, the Balanced Scorecard (BSC) and Artificial Neural Networks (ANNs) are intertwined with forecasting the outcomes of a co-financed EU program by means of its impact on the non-financial measures of the government body that materialized it. The predictive accuracy of the present model advanced in this research study takes into account all the complexities of the business environment, within which the provided dataset is produced. The outcomes of the study showed that the measures taken to enhance customer satisfaction allows for further improvement. The utilization of the proposed model could facilitate the decision-making process and initiate changes to the administrational issues of the available funding programs.

Farmers' knowledge and practice regarding good agricultural practices (GAP) on safe pesticide usage in Indonesia

Synthetic pesticides are widely applied for pest and disease control in Indonesia. However, a lack of knowledge and use of Good Agricultural Practices (GAP) for safe pesticide usage among Indonesian farmers remains a problem. This study aims to investigate the gap between farmers' knowledge of GAP for safe pesticide usage and their application of it. This research was conducted in 2020 in five Indonesian provinces. Primary data collection was by means of a survey, in which 298 respondents answered structured questionnaires. The survey also identified the sources of the information recorded and the respondents’ experience of pesticide exposure. The analysis tools used were the Wilcoxon Signed Ranked Test and Importance-Performance Analysis (IPA). There were significant differences in the results of the first analysis. These results appear to confirm the results of further analysis using IPA, which show that a high level of knowledge does not mean that farmers will apply this knowledge in practice: this is particularly relevant to wearing gloves and masks, using tools to remove blockages, never clearing blocked nozzles by blowing into them, and disposing of empty containers properly. Nevertheless, in some cases high levels of knowledge do result in high levels of application. Cases of pesticide exposure affecting human health by causing symptoms such as dizziness, nausea, and vomiting confirm that GAP for pesticide usage are not being implemented properly by some farmers. It is therefore recommended that their knowledge should be enhanced through the series of technical training programs using participatory approaches, so that farmers accumulate knowledge which will drive them to adopt GAP for safe pesticide usage.

Efficiency Analysis as a Tool for Revealing Best Practices and Innovations: The Case of the Sheep Meat Sector in Europe

Simple Summary

The European sheep meat sector faces technical, market and financial challenges that threaten its economic performance and overall sustainability. At the same time, the sector is characterized by poor and slow adoption of innovations that could help towards facing these challenges. In this study, the technical efficiency of extensive, semi-intensive and intensive sheep meat farms in France, Spain and the UK was explored to reveal the profile of the most efficient ones and identify the best practices and innovations that these farms apply. The most efficient sheep meat farms reared large flocks, used available infrastructure at full capacity and managed human labor in a rational way. These best farms emphasized feeding and grazing innovations, marketing strategies, breeding programs and use of digital technologies. The uptake of such practices and innovations by farms of similar production systems could help to increase the productivity and economic performance of the sheep meat sector.

Abstract

The slow adoption of innovations is a key challenge that the European sheep sector faces for its sustainability. The future of the sector lies on the adoption of best practices, modern technologies and innovations that can improve its resilience and mitigate its dependence on public support. In this study, the concept of technical efficiency was used to reveal the most efficient sheep meat farms and to identify the best practices and farm innovations that could potentially be adopted by other farms of similar production systems. Data Envelopment Analysis was applied to farm accounting data from 458 sheep meat farms of intensive, semi-intensive and extensive systems from France, Spain and the UK, and the structural and economic characteristics of the most efficient farms were analyzed. These best farmers were indicated through a survey, which was conducted within the Innovation for Sustainable Sheep and Goat Production in the Europe (iSAGE) Horizon 2020 project, the management and production practices and innovations that improve their economic performance and make them better than their peers.