How can we make sense of smart technologies for sustainable agriculture? - A discussion paper

This paper discusses the challenges of assessing the benefits and risks of new digital technologies, so-called ‘smart technologies’ for sustainable agri-food systems. It builds on the results of a literature review that was embedded in a wider study on future options for (sustainable) farming systems in Germany. Following the concepts of Actor-Network-Theory, we can conceive of smart technologies in agriculture as networks that can only be understood in their entirety when considering the relationships with all actors involved: technology developers, users (farmers, consumers and others), data analysts, legal regulators, policy makers, and potential others. Furthermore, interaction of the technology and its implementers with nature, such as plants, entire landscapes, and animals, need to be taken into consideration. As a consequence, we have to deal with a highly complex system when assessing the technology – at a time where many of the relevant questions have not been sufficiently researched yet. Building on the FAO’s SAFA guidelines, the paper outlines criteria against which smart technologies could be assessed for their potential to contribute to a sustainable development of agri-food systems. These include aspects of governance, ecology, economy and social issues. We draw some tentative conclusions on the required framework conditions for implementation of digital technology, in particular from the perspective of sustainable agriculture. These are aimed at fuelling further discussion about the potentials and risks of the technology

Solution for remote real-time visual expertise of agricultural objects

ArticleIn recent years automated image and video analyses of plants and animals have become important techniques in Pre cision Agriculture for the detection of anomalies in development. Unlikely, machine learning (i.e., artificial neural networks, support vector machine, and other relevant techniques) are not always able to support decision making. Nevertheless, experts can use these techniques for developing more precise solutions and analysis approaches. It is labour - intensive and time - consuming for the experts to continuously visit the production sites to make direct on - site observations. Therefore, videos from the site n eed to be made available for remote viewing and analysis. In some cases it is also essential to monitor different parts of objects in agriculture and animal farming (e.g., bottom of the plants, stomach of the animal, etc.) which are difficult to access in standard recording procedures. One possible solution for the farmer is the use of a portable camera with real - streaming option r ather than a stationary camera. The aim of this paper is the proposition of a solution for real - time video streaming of agricultural objects (plants and/or animals) for remote expert evaluation and diagnosis. The proposed system is based on a Raspberry Pi 3, which is used to transfer the video from the attached camera to the YouTube streaming service. Users will be able to watch the video stream from the YouTube service on any device that has a web browser. Several cameras (USB, and Raspberry Pi camera) and video resolutions (from 480p till 1 , 080p) are compared and analysed, to find the best option, taking into account video quality, frame rates, and latency. Energy consumption of the whole system is evaluated and for the chosen solution it is 645 mA

An ethogram of biter and bitten pigs during an ear biting event: first step in the development of a Precision Livestock Farming tool

peer-reviewedPigs reared in intensive farming systems are more likely to develop damaging behaviours such as tail and ear biting (EB) due to their difficulty in coping with the environment and their inability to perform natural behaviours. However, much less is known about the aetiology of EB behaviour compared to tail biting behaviour. Application of new intervention strategies may be the key to deal with this welfare issue. The discipline of Precision Livestock Farming (PLF) allows farmers to improve their management practices with the use of advanced technologies. Exploring the behaviour is the first step to identify reliable indicators for the development of such a tool. Therefore, the aim of this study was to develop an ethogram of biter and bitten pigs during an EB event and to find potential features for the development of a tool that can monitor EB events automatically and continuously. The observational study was carried out on a 300 sow farrow-to-finish commercial farm in Ireland (Co. Cork) during the first and second weaner stages. Three pens per stage holding c. 35 pigs each, six pens in total, were video recorded and 2.2 h of videos per pen were selected for video analysis. Two ethograms were developed, one for the biter and one for the bitten pig, to describe their behavioural repertoire. Behaviours were audio-visually labelled using ELAN and afterwards the resulting labels were processed using MATLAB® 2014. For the video data, duration and frequency of the observed behavioural interactions were quantified. Six behaviours were identified for the biter pig and a total of 710 interactions were observed: chewing (215 cases), quick bite (138 cases), pulling ear (97 cases), shaking head (11 cases), gentle manipulation (129 cases) and attempt to EB (93 cases). When the behaviour observed was not certain, it was classified as doubt (27 cases). Seven behaviours were identified for the bitten pig in response to the biters behaviour and were divided in: four non-vocal behaviours described as biting (40 cases), head knocking (209 cases), shaking/moving head (225 cases) or moving away (156 cases); and three vocal behaviours identified as scream (74 cases), grunt (166 cases), and squeal (125 cases). Vocal behaviours were classified using a verified set of features yielding a precision of 83.2%. A significant difference in duration was found between all the behaviours (P < 0.001), except between gentle manipulation and chewing where no difference in duration was found (P < 0.338). The results illustrate the heterogeneity of EB behaviours, which may be used to better understand this poorly studied damaging behaviour. They also indicate potential for the development of a PLF tool to automatically, continuously monitor such behaviour on farm by combining the behaviour of the biter pig and the bitten pigs responses

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: a systematic review

Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced

Tracking agonistic behaviors in pigs

Master of ScienceDepartment of Animal Sciences and IndustryLindsey E HulbertModern day animal production is intensively increasing to meet global demand for animal products. Producers must balance the increased demand for animal product and instill trust in consumers. Pigs raised in intensive production system display more fighting and unresolved conflict than wildtype pigs. This conflict is called “agonistic interactions”. These undesired behaviors occur mainly at the finishing stage of pigs when resources (water, food, space etc.) becomes limited or when animals meet unfamiliar pen mates. Chronic stress from unresolved conflict is an indication of poor animal welfare and may lead to reduced product quality. The first step in reducing the conflict is finding an efficient system to detect and track pigs at the individual level. Precision animal management is the incorporation of information technology into animal production to monitor animals online, which are supported with artificial intelligence to collect and analyze data that will help to sustainably improve livestock farming. While many systems exist, visual tracking has a great potential for commercial application because it is the least invasive. These systems will, therefore, be useful to producers by providing an early detection of agonistic behaviors in herd, provide timely intervention to compromised animals thereby increasing economic gains

Influence of body mass, tryptophan concentration and certain environmental factors on behavior and production parameters of weaned piglets

Istraživanje je sprovedeno prema postavljenom cilju na eksperimentalnoj farmi Instituta za stočarstvo, Beograd - Zemun. Istraživanje je obuhvatalo 432 zalučene prasadi oba pola, rasa veliki jorkšir i švedski landras, kao i meleze tih rasa. Prasad su uvedena u istraživanje nakon odbijanja (u starosti od 30 dana), kada su izmerene telesne mase. Na osnovu telesne mase i pola su formirane homogene grupe (osim u tretmanu sa neujednačenim telesnim masama). U svakoj grupi je bilo po 7 jedinki (4 muška i 3 ženska praseta), osim u trećem tretmanu, kada je bio ispitivan uticaj gustine naseljenosti. Ispitivanja su trajala do šestog dana po zalučenju, kada je bilo i završno merenje telesne mase. Svi tretmani podrazumevali su formiranje 4 grupe prasadi - kontrolna (KG) i ispitivane grupe (IG1, IG2 i IG3) prema sledećem rasporedu: u prvom tretmanu koristio se L-triptofan kao dodatak potpunoj smeši u 3 različite koncentracije (za IG1, IG2 i IG3, redom: 0,1%, 0,2% i 0,3% u starter-smešama sa početkom od 10 dana pre uvođenja u istraživanje i tokom trajanja istraživanja); u drugom tretmanu se pratio uticaj intenziteta osvetljenja (KG - 60 lx, pojačano osvetljenje za IG1 i IG2 - 100 i 150 lx, redom, i smanjeno osvetljenje za IG3 - 40 lx); u trećem tretmanu je bio ispitivan uticaj gustine naseljenosti (KG -7, povećana gustina naseljenosti za IG3 i IG2 – 11 i 9 prasadi, smanjena za IG1 – 5 prasadi u kavezu); u četvrtom se pratio uticaj telesne mase (KG – 7 prasadi približne telesne mase, za IG1 – 2 teža i 5 lakših prasadi, za IG2 - 3 teža i 4 lakša praseta, i IG3 - 5 težih i 2 lakša praseta); i u petom tretmanu se ispitivao uticaj obogaćenja sredine (crvena lopta prečnika 10 cm sa kracima (IG1), pamučna užad dužine 40 cm okačena za stranice kaveza (IG2) i slama 200g dnevno na punom delu poda (IG3)) na ponašanje i proizvodne rezultate. Tokom istraživanja praćeni su prozvodni parametri (konzumacija hrane, dnevni prirast i konverzija hrane), kompletno ponašanje (pomoću kamera) i parametri krvi (Pig-MAP, laktati i haptoglobin). Dodatak triptofana u standardnu farmsku smešu nije značajno uticao na proizvodne parametre i na parametre krvi, ali je imao pozitivan efekat na izmenu obrazaca ponašanja kod prasadi. Različiti intenziteti osvetljenja nisu značajno uticali na proizvodne parametre i ponašanje kod prasadi, ali su značajno uticali (p<0,05) na koncentraciju laktata u krvi. Povećana gustina naseljenosti je značajno pozitivno uticala na proizvodne parametre (prosečan dnavni prirast i konverziju hrane) (p<0,05) i na koncentraciju Pig-Map-a u krvi (p<0,05; p<0,01), dok je negativno uticala na pojavu agresije i griže kod prasadi (p<0,05). Telesna masa je značajno i veoma značajno uticala na koncentraciju Pig-MAP-a (p<0,05; p<0,01) i laktata (p<0,05) između ispitivanih grupa. Obogađivanje sredine je značajno uticalo (p<0,05) na smanjenje broja konflikata i griže između ispitivanih grupa prasadi. Slama se pokazala kao najbolji material za obogaćivanje sredine u istraživanju. Obogaćivanje sredine je pozitivno uticalo (p<0,05) na poboljšanje aftektivnih stanja kod prasadi tokom prvih 24 časa nakon formiranja grupa.According to the set goal, the research was carried out at the experimental farm of the Institute for Animal Husbandry, Belgrade - Zemun. The research included 432 reared piglets of both sexes, Large White and Swedish Landrace breeds, as well as their crossbreeds. Piglets were introduced into the research after weaning (at the age of 30 days) when their body weights were measured. Homogeneous groups were formed based on body mass and gender (except in the trial with uneven body mass). There were 7 individuals in each group (4 male and 3 female pigs), except in the third treatment when the influence of population density was examined. The examinations lasted until the sixth day after the mixing when the final measurement of body weight took place. All treatments were based on the formation of 4 groups of piglets - control (KG) and test groups (IG1, IG2 and IG3) according to the following schedule: in the first treatment, L-tryptophan was used as an addition to the complete mixture in 3 different concentrations (for IG1, IG2 and IG3 , respectively: 0.1%, 0.2% and 0.3% in starter-mixtures starting 10 days before introduction into the experiment and during the research); in the second treatment, the influence of lighting intensity was monitored (KG - 60 lx, increased lighting for IG1 and IG2 - 100 and 150 lx, and reduced lighting for IG3 - 40 lx); in the third treatment, the influence of population density was examined (KG -7, increased population density for IG3 and IG2 – 11 and 9 piglets, decreased for IG1 – 5 piglets in a cage); in the fourth, the influence of body weight was monitored (KG - 7 piglets of similar body weight, for IG1 - 2 heavier and 5 lighter piglets, for IG2 - 3 heavier and 4 lighter piglets, and IG3 - 5 heavier and 2 lighter piglets); and in the fifth treatment, the influence of environmental enrichment (a red ball with arms with a diameter of 10 cm (IG1), cotton ropes 40 cm long suspended from the sides of the cage (IG2) and straw 200g per day on the full part of the floor (IG3)) on behavior and production results was examined. During the experiment, production parameters (food consumption, daily gain and feed conversion), complete behaviour (using cameras) and blood parameters (Pig-MAP, lactates and haptoglobin) were monitored. The addition of tryptophan to the standard farm mixture did not significantly affect the production parameters and the blood parameters, but it had a positive effect on changing the behaviour patterns of the piglets. Different lighting regimes did not affect the production parameters and behaviour of piglets, but they affected (p<0.05) the concentration of lactate in the blood. Increased population density had a significant positive effect on production parameters (average daily gain and feed conversion) (p<0.05), on Pig-Map concentration in blood (p<0.05; p<0.01), while it had a negative effect on the aggression and biting in piglets (p<0.05). Body mass significantly and very significantly influenced the concentration of Pig-MAP (p<0.05; p<0.01) and lactate (p<0.05) between the studied groups. Environment enrichment had a significant effect (p<0.05) on the reduction of the number of conflicts and biting between the examined groups of piglets. Straw proved was the best manipulative material in the experiment. Enrichment of the environment had a positive effect (p<0.05) on the improvement of affective states in piglets during the first 24 hours after the formation of groups