Agricultural and Food Science

Acknowledgement of referees

2024-03-30T14:24:29+02:00

Acknowledgement

The future of agriculture and agricultural policy: perceptions of non-farmers and farmers

2023-11-29T12:27:46+02:00

We examined the views of Finnish non-farmers and farmers on the desirable future developments of agriculture and agricultural policy using principal component and cluster analysis by focusing on three key themes: the structure of agricultural production, agri-environmental issues, and the funding of agriculture. There is strong public support for maintaining the viability of domestic agriculture through government intervention, but views differ on allocation of agricultural support and how agricultural production should be developed. A significant number of respondents supported the idea that climate and other environmental issues should be better considered in agricultural policy. However, about half of the respondents accepted environmental damage caused by agriculture and one-fifth perceived the importance of agriculture in a society as declining. These views, not prominent in the public debate, emphasize the importance of regular investigation of citizen opinions for including all the relevant stands in policy discussion to design legitimate policy measures.

Protein recovery from yellow peas (Pisum sativum L.) for enhanced processing sustainability and functional properties

2024-02-09T09:15:26+02:00

This research focuses on sustainable protein recovery methods from a new yellow pea variety by examining alternative
pH-shifting processes. The study focuses on reducing water consumption during alkaline extraction by adjusting
solid-liquid ratios, and evaluating the impact of various isoelectric precipitants, including lactic acid and lactic acid
bacteria (Lactobacillus plantarum and Lactobacillus lactis), on the functional and antioxidant properties of products
across a wide range of pH values. It was here found that the process alternative with three 1:10 (w/v) extraction
cycles and lactic acid bacteria as precipitant agent achieved high process productivity (0.36 kg protein product/kg
pea flour) and low specific water consumption (94.9 kg water/kg protein product). No significant differences were
observed in protein content and yield when compared to other flour-water ratios with higher water consumption
or less eco-friendly precipitants. Products precipitated with lactic acid bacteria formed stable emulsions even at the
isoelectric point, exhibited superior free radical scavenging activity, although solubility and water holding capacity
were lower, and no differences were noted in oil holding capacity, foaming capacity, and foam stability.

The meaning of animal well-being for farmers and dairy farm employees

2024-02-23T13:41:58+02:00

Dairy farms in Sweden have undergone a structural change. The number of family farms has decreased, while the number of large dairy farms with employees caring for the animals has increased. This changing situation has created a new farming landscape. From that perspective, it is crucial to gain insight into what factors contribute to the well-being of humans and animals on big dairy farms. Twenty-three semi-structured interviews were conducted on three farms. Farmers and employees were interviewed. The material was analysed using a qualitative approach inspired by qualitative content analysis. For the farmers and employees, animal well-being was central for various reasons and from different perspectives. Despite the differences, the impact of animal well-being was interlinked between the two groups. An increased and deeper understanding of the different perspectives and needs arising from the different roles of farmers and employees can provide new knowledge about factors important for improving
animal well-being.

Convolutional neural network model for the qualitative evaluation of geometric shape of carrot root

2024-02-14T06:41:38+02:00

The main objective of the study is the development of an automatic carrot root classification model, marked as CR-NET, with the use of a Convolutional Neural Network (CNN). CNN with a constant architecture was built, consisting
of an alternating arrangement of five Conv2D, MaxPooling2D and Dropout classes, for which in the Python 3.9
programming language a calculation algorithm was developed. It was found that the classification process of the carrot root images was carried out with an accuracy of 89.06%, meaning that 50 images were misclassified. The highest number of 21 erroneously classified photographs were from the extra class, of which 15 to the first class, thus not resulting in significant loss. However, assuming the number of refuse as the classification basis, the model accuracy greatly increases to 98.69%, as only 6 photographs were erroneously assigned.