Diminished work ability as a contributing factor for farmer’s interest in switching to organic production

T.E.A. Mattila¹*, E. Liski¹, M. Väre¹ and R.H. Rautiainen²

¹Natural Resources Institution Finland (‘Luke’), Latokartanonkaari 9, FI-00790 Helsinki, Finland
²University of Nebraska Medical Center, Omaha, Nebraska, 68198-4388, USA
*Correspondence: tiina.mattila@luke.fi

Abstract:

Previous studies suggest organic producers have diminished work ability, but it is unclear if this is due to pre-existing conditions or work exposures in organic production itself. The current study explored whether diminished work ability is a contributing factor to the interest in switching from conventional to organic production. The study used data from 2018, Finnish farmer questionnaire, analysed by machine learning – based approach and logistic regression modelling. Nearly half (46%) of the survey respondents (n = 2,948) had a diminished work ability score. Seventeen percent (n = 501) of the respondents reported being interested in switching to organic production. Farmers with diminished work ability had greater odds (OR 1.56, 95% CI: 1.26–1.92) for showing interest in switching. Those growing horticulture and special crops (vs. cereals) (OR 0.55) and those age 55+ years (vs. less than 35) (OR 0.51) showed less interest in switching. The interest in starting or expanding organic production was higher among those who already had an organic agreement on part of their farm (OR 5.7) and those who had other business activities on the farm (OR 1.36). In summary, this study suggests that diminished work ability predicts farmer’s interest for switching to organic production. Measures to protect the health and well-being of farmers and workers during and after switching to organic production is critically important in achieving not only policy goals to increase organic production, but also good quality of life of farmers.

Key words:

agriculture, logistic regression, machine learning, social sustainability, well-being at work

Apple scab detection using CNN and Transfer Learning

S. Kodors¹*, G. Lacis², O. Sokolova2,V. Zhukovs¹, I. Apeinans¹ and T. Bartulsons²

¹Rezekne Academy of Technologies, Faculty of Engineering, Institute of Engineering, Atbrivoshanas Str. 115, LV-4601 Rezekne, Latvia
²Institute of Horticulture, Graudu Str. 1, LV-3701 Ceriņi, Krimūnu pagasts, Dobeles novads, Latvia
*Correspondence: sergejs.kodors@rta.lv

Abstract:

The goal of smart and precise horticulture is to increase yield and product quality by simultaneous reduction of pesticide application, thereby promoting the improvement of food security. The scope of this research is apple scab detection in the early stage of development using mobile phones and artificial intelligence based on convolutional neural network (CNN) applications. The research considers data acquisition and CNN training. Two datasets were collected – with images of scab infected fruits and leaves of an apple tree. However, data acquisition is a time-consuming process and scab appearance has a probability factor. Therefore, transfer learning is an appropriate training methodology. The goal of this research was to select the most suitable dataset for transfer learning for the apple scab detection domain and to evaluate the transfer learning impact comparing it with learning from scratch. The statistical analysis confirmed the positive effect of transfer learning on CNN performance with significance level 0.05.

Key words: