Agricultural and Food Science 2024-03-31T08:28:49+03:00 Tuula Puhakainen Open Journal Systems <p>Agricultural and Food Science (AFSci) is a peer-reviewed journal, published quarterly. AFSci publishes original research reports on agriculture and food research in relation to primary production in boreal agriculture. Acceptable papers must be of international interest and have a northern dimension. We especially welcome papers related to agriculture in Boreal and Baltic Sea Region.</p> Acknowledgement of referees 2024-03-30T14:24:29+02:00 Tuula Puhakainen <p>Acknowledgement</p> 2024-03-31T00:00:00+02:00 Copyright (c) 2024 Tuula Puhakainen The future of agriculture and agricultural policy: perceptions of non-farmers and farmers 2023-11-29T12:27:46+02:00 Annika Tienhaara Jyrki Niemi Annukka Vainio Eija Pouta <p>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.</p> 2024-03-31T00:00:00+02:00 Copyright (c) 2024 Annika Tienhaara, Jyrki Niemi, Annukka Vainio, Eija Pouta Protein recovery from yellow peas (Pisum sativum L.) for enhanced processing sustainability and functional properties 2024-02-09T09:15:26+02:00 María Angeles Guraya María Nieves Andrín Rocío Batres Pablo Antonio Torresi Maria Agustina Reinheimer Ezequiel Godoy <p>This research focuses on sustainable protein recovery methods from a new yellow pea variety by examining alternative<br />pH-shifting processes. The study focuses on reducing water consumption during alkaline extraction by adjusting<br />solid-liquid ratios, and evaluating the impact of various isoelectric precipitants, including lactic acid and lactic acid<br />bacteria (<em>Lactobacillus</em> <em>plantarum</em> and <em>Lactobacillus</em> <em>lactis</em>), on the functional and antioxidant properties of products<br />across a wide range of pH values. It was here found that the process alternative with three 1:10 (w/v) extraction<br />cycles and lactic acid bacteria as precipitant agent achieved high process productivity (0.36 kg protein product/kg<br />pea flour) and low specific water consumption (94.9 kg water/kg protein product). No significant differences were<br />observed in protein content and yield when compared to other flour-water ratios with higher water consumption<br />or less eco-friendly precipitants. Products precipitated with lactic acid bacteria formed stable emulsions even at the<br />isoelectric point, exhibited superior free radical scavenging activity, although solubility and water holding capacity<br />were lower, and no differences were noted in oil holding capacity, foaming capacity, and foam stability.</p> 2024-03-31T00:00:00+02:00 Copyright (c) 2024 María Angeles Guraya, María Nieves Andrín, Rocío Batres, Pablo Antonio Torresi , Maria Agustina Reinheimer, Ezequiel Godoy The meaning of animal well-being for farmers and dairy farm employees 2024-02-23T13:41:58+02:00 Louise Axelsson Katarina Arvidsson – Segerkvist Anna María Pálsdóttir Magnus Ljung <p>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<br />animal well-being.</p> 2024-03-31T00:00:00+02:00 Copyright (c) 2024 Louise Axelsson, Katarina Arvidsson – Segerkvist, Anna María Pálsdóttir, Magnus Ljung Convolutional neural network model for the qualitative evaluation of geometric shape of carrot root 2024-02-14T06:41:38+02:00 Piotr Rybacki Zuzanna Sawinska Miroslava Kačániová Przemysław Ł. Kowalczewski Andrzej Osuch Karol Durczak <p>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<br />of an alternating arrangement of five Conv2D, MaxPooling2D and Dropout classes, for which in the Python 3.9 <br />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.</p> 2024-03-31T00:00:00+02:00 Copyright (c) 2024 Piotr Rybacki, Zuzanna Sawinska, Miroslava Kačániová, Przemysław Ł. Kowalczewski, Andrzej Osuch, Karol Durczak