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opencc-by-4.0Jan 2023View details →

ArMoR Cluster: 7 research projects fight Antimicrobial Resistance in livestock farming (updated version)

Supported by the European Commission, Horizon Dissemination Booster (HRB) contributes to an effective transfer of research and innovation project results to policy makers, industry and society by offering various services as dissemination, exploitation strategy and business plan development to projects. Within Horizon Results Booster programme (HRB), 7 research projects AMRILS, AVANT, BM-FARM, FARMCARE, DISARM, HealthyLivestock and ROADMAP have formed the "ArMoR Cluster" to develop a conceptual framework to improve understanding of AMR in livestock systems. The video is available on YouTube: <a href="https://www.youtube.com/watch?v=ACbnyu3PhOY">https://www.youtube.com/watch?v=ACbnyu3PhOY</a> For any further questions please contact us at: <ul> <li><a href="https://zenodo.org/record/avant@rtds-group.com">avant@rtds-group.com</a> (project AVANT)</li> </ul>

opencc-by-4.0Feb 2023View details →

Microfarms and computational agriculture: a future of farming?

The following video introduces the Robots for Microfarms (ROMI) project and describes the concept of Micro Farms and Computational Agriculture. Funded by EU Grant 773875. Videos are available in: <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> Video script: (MINCHIN) Micro-Farms are farms which are smaller than five hectares and that is a European commission designation of that type of farm which are very common and proliferate all across Europe and are growing in number. (HANAPPE) Micro-farms the way I see it now would be a small farm 1000 square meters maybe a bit more using poly-cropping to really optimise the space and situated next to the city, with the direct link to the city. (MINCHIN) But the Farms themselves are very interesting because being a small scale they cannot compete with a conventional agriculture and for that reason often turn to more diverse crops, polyculture systems and diversity as a solution for their markets. difference information (SERRA CANTí) An organic farm is a space where the coal is to cultivate the entire ecosystem, and to do so we focus on different crops that help and complement between them, and are not monocultures. Rotating crop beds, adding organic matter, structuring the soil and taking care of the life in the soil such as microorganisms, bacteria, fungi. All of this ultimately ensure that the plant is healthy and does not have so many pests and diseases. A conventional field can have many hectares with the same crop, they are monocultures. That means that if one disease comes, or one insect it can reproduce exponentially. But instead if you have different crops, this stops because the food that it has is limited. (MINCHIN) When we work with Organic agriculture we're really looking to a tradition of companion planting and polyculture which is much more complex than the conventional agriculture of today. So what we're seeing here is planting of tomatoes together with lettuce and together with a basil and the Basils give a natural defence against predators for the tomatoes but we can also use an intercrop lettuces in the gaps. But dealing with that polyculture becomes rather complex. (HANAPPE) And managing its complexity is difficult of course that's one of the reasons why we went to monoculture in the first place. But now with the digital tools that we have we can reintroduce some of this complexity and handle AI and Robotics to assist us in managing this complexity. (MINCHIN) Farmers working on a small scale with a large diversity of crop finds that the work is often very very labor intensive. (HANAPPE) For two months I've experienced myself at the Chatelain farm and in it it's quite taxing on the knees and the back. Ninety Five percent of the disorders in agriculture are muscular skeletal problems. (MINCHIN) The statistics show that after four years of starting a new Farm, many of these young Farmers actually end up with back problems and really struggle to manage the intensity of that biodiversity, of that diversity of crop. So for ROMI our challenge really is to try and support those young Farmers to deal with the complexity and to manage some of the menial tasks that they face day to day. (BAORI) If we can help a small organic micro-farm stay in business, because we're helping to replace a little bit of the manual labor and the the kind of hardship of that keep their costs down it means that we're encouraging smaller Farms to not use chemicals they can use a Rover for weeding, and it's better for for the land it's better for the farmers it's better for us all really in the end so it's a very worthwhile project. (CAMPRODON) Agricultural itself is technology right it's not lets say we don't wake up one day and suddenly the world looked like it is, no, we built it for thousands of years. And so for me I don't know if there's such a big disconnection or we should think as a big disconnection between traditional farming and this new farming. Things change but I like to see it more as a natural evolution of the way that humans use technology. Basically they develop this cultural practice, that helps them to modify their environment and they apply that to farming. (HANAPPE) What can we do with new technology to help these farms? And then it was at some point you know just sort of well, let's do something very provocative, let's do robotics for these farms. Which was a bit of a clash because many of these farmers actually started farming because they sort of wanted to go back to to Nature. And then saying to them look well we can introduce robotics for you, is a bit of a sort of edgy topic. (MINCHIN) And that really means that we might be able to take the best of the past and combine it with the tools of the future to forge a ‘computational agriculture’ which feeds the land, feeds the ecology and also feeds ourselves. The ROMI tools are really set up to be able to help farmers with the support of biologists and with the support of computation, but it's not there yet. That's why we have open sourced these tools to allow a future development towards those objectives.

opencc-by-4.0Feb 2023View details →

The ROMI platform: a helping hand to farming communities

The following video describes the ROMI platform and explains how the set of tools can be adapted to different farming contexts. Funded by EU Grant 773875. Videos are available in: <ul> <li>Hi-res (1080p Apple ProRes)</li> <li>Mid-res (1080p H265)</li> </ul> Video script: (MINCHIN) ROMI is robotics for micro-farms. It consists of a platform of robotic tools that can be customised, adapted to local situations. So understanding that these tools can be used in different ways is absolutely crucial to the project. (HANAPPE) We try to work on a number of tools that are open hardware, like the cable bot like the rover like the plant imager these are bricks that can be reused in different contexts different types of farms and that gradually what we want to build upon. (MINCHIN) Over four years of development and testing we've developed a platform of tools that can work together and that really goes from the ground-based rover which has got an ability to move around a plant and move to a plant in a field. We have above that a cable system, a cable bot which can be strung in a greenhouse situation or along a polyculture farm such as this one. We are using drones to be able to have an overall vision of the landscape and changes within the environment finally there's a phenotyping robot which is able to scan in detail a plant in a laboratory to help biologists or plant development biologists understand and map growth. These come together in what we're calling the farmers dashboard which allows somebody in situ to understand what they're seeing understand and analyse things which they can't see, and also provide data and data sets back into a laboratory or a computational situation to try and track and understand a complexity in much more depth, than has ever been able to be done before. (BAORI) Agricultural equipment in general you're looking at a hundred thousand pounds hundred thousand euros you name your your kind of uh your range but you're not accessible to someone who's got a very small farm okay so you're looking at a lot of money um the rover at the moment costs approximately seven to eight thousand euros to build. Now as a piece of industrial equipment, agricultural equipment that's that's very very cheap. We do have a very simple web interface that you can run on your phone to connect to the robot to tell it; I want a hoe for the next 25 meters, I want to do weeding. We use a playstation controller actually for just driving the rover around the field and this is something that anybody can learn to use very very quickly. We're kind of targeting somebody with very minimal technical knowledge so although you can build this and if you're a maker you can change the code you can change it to do what you want. We kind of envisage a situation where actually people will just download a complete package put it onto an Arduino, put it onto the raspberry pi. In fact that will install everything and then you're ready to go. (HANAPPE) The rover we intend to be the sort of most practical device, we've specialised it for weeding which is something for which there is a demand today. (OKADA) Our enemies i would say are the weeds as you can see and in summer it's terrible. (MINCHIN) One of the crucial demonstrators of the ROMI project is the weeding tool. Farmers dealing with complexity of a number of crops spend at least twenty percent of their time weeding. When we are able to weed mechanically this means that we don't use herbicides. And so weeding mechanically affords us a more organic way of agriculture and in this way the the weeding tool developed by ROMI is a demonstrator of what could be done to support some of these farmers dealing with a labor-intensive complexity on their farm. but the software the programming and the drivers of that sort of computation don't stop at weeding and we are able to go beyond mechanically into a whole potential of solutions. (HANAPPE) We would like to get them, these tools, in the field as soon as possible so that we can work with farmers and have their feedback but at the same time try to build a community around it, multi-disciplinary of farmers, engineers, researchers to improve these tools, augment their possibilities and then we'll see how far we get. (BESNARD) the ROMI project is interested in getting important information from the plant architecture and from the plant growth process it's very important for a farmer to get this information just to detect very early on if possible any deviations from the normal curves, if you have a pathogen attack for example you just notice very early sign of stress in your plants, and that's generally the pathogen is not like everywhere so it's much easier to to counter attack especially like in organic farming when you know like a lot of chemicals to do it. And at the same time we are developing a tool a technology to get as much information as we can from plant architecture and this has an application towards farmers to just monitor the growth of their crops. But this can have also another application for basic scientists like me just to understand the basic process of our plant built its own architecture which is a basic question.

opencc-by-4.0Feb 2023View details →

Data for the article "Organic farming increases functional diversity and ecosystem service provision of spontaneous vegetation in Mediterranean vineyards"

Research data for the article "Organic farming increases functional diversity and ecosystem service provision of spontaneous vegetation in Mediterranean vineyards" made by Roser Rotchés-Ribalta, Joan Marull and Joan Pino published in Ecological Indicators. It includes: - Species and functional diversities of spontaneous plant species in the different plots surveyed per field - The cover of each species per plot in each field - The functional traits used for each species - The physico-chemical soil data of each plot per field

FIGURE 3 in New species and records of earthworms (Annelida, Oligochaeta: Crassiclitellata) in small holder farming systems in the Peruvian Amazon

FIGURE 3. Martiodrilus (Maipure) tapiai sp. nov. Holotype. A. Common seta, segment 36. B. Genital setae a, segment 15. C. External, ventral view of clitellum and tubercula pubertatis, segments 14–25. D. Spermathecae of 6, 7, 8, 9; Gizzard in segment 6.

FIGURE 4 in New species and records of earthworms (Annelida, Oligochaeta: Crassiclitellata) in small holder farming systems in the Peruvian Amazon

FIGURE 4. Martiodrilus (Martiodrilus) alegrei sp. nov. Holotype. A. Common seta, segment 32. B. Genital setae a, segment 17. C. External, ventral view of clitellum and tubercula pubertatis, segments 15 - 27. D. Calciferous glands of segment 8. E. Spermathecae of 6.

FIGURE 2 in New species and records of earthworms (Annelida, Oligochaeta: Crassiclitellata) in small holder farming systems in the Peruvian Amazon

FIGURE 2. Martiodrilus (Maipure) yurimaguensis sp. nov. Holotype. A. Common seta, segment 56. B. Genital setae a, segment 17. C. External, ventral view of clitellum and tubercula pubertatis, segments 14–1/4 25. D. Calciferous glands segment 8. E. Calciferous glands segment 11. F. Spermathecae of 6, 7, 8, 9.

FIGURE 1 in New species and records of earthworms (Annelida, Oligochaeta: Crassiclitellata) in small holder farming systems in the Peruvian Amazon

FIGURE 1. Righiodrilus omagua sp. nov. Holotype. A. External lateral view of anterior body (preserved), showing clitellum in 15–20. B. Internal anatomy of anterior segments, dorsal view. S = Spermathecae of 9, 10; CG = calciferous glands in segments 11, 12; SV= large seminal vesicles in segment 12 extending backwards to segment 24.

opencc-by-4.0May 2023View details →

Online survey data: Exploring the socio-economic dynamics and innovation capacities of rural food and farming microbusinesses

Online survey data which, in compilation with focus group data, formed the basis for our NICRE-funded report into the socio-economic dynamics and innovation capacities of rural food and farming microbusinesses. Full report is available here: https://eprints.glos.ac.uk/11449/

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Biodiversity and pollination benefits trade off against profit in an intensive farming system

Agricultural expansion and intensification have boosted global food production but have come at the cost of environmental degradation and biodiversity loss. Biodiversity-friendly farming that boosts ecosystem services, such as pollination and natural pest control, is widely being advocated to maintain and improve agricultural productivity while safeguarding biodiversity. A vast body of evidence showing the agronomic benefits of enhanced ecosystem service delivery represents important incentives to adopt practices enhancing biodiversity. However, the costs of biodiversity-friendly management are rarely taken into account and may represent a major barrier impeding uptake by farmers. Whether and how biodiversity conservation, ecosystem service delivery, and farm profit can go hand in hand is unknown. Here we quantify the ecological, agronomic and net economic benefits of biodiversity-friendly farming in an intensive grassland-sunflower system in southwest France. We found that reducing land-use intensity on agricultural grasslands drastically enhances flower availability and wild bee diversity, including rare species. Biodiversity-friendly management of grasslands furthermore resulted in an up to 17% higher revenue on neighboring sunflower fields through positive effects on pollination service delivery. However, the opportunity costs of reduced grassland forage yields consistently exceeded the economic benefits of enhanced sunflower pollination. Our results highlight that profitability is often a key constraint hampering adoption of biodiversity-based farming and uptake critically depends on society's willingness to pay for associated delivery of public goods such as biodiversity.

opencc-zeroDec 2022View details →

opencc-by-4.0Aug 2023View details →

Primary Survey Socioeconomic Dataset of the Study on Agricultural Performance and Farm Size: Village Dynamics in South Asia (VDSA)

These datasets contain many economic variables related to agriculture like crop output value, profit and several others.   These datasets can be used for testing several hypotheses related to agricultural economics, both at plot level and household level. Users can also reproduce these datasets using the STATA 14 do file ‘VDSA data management for agricultural performance’. This STATA program file uses the Village Dynamics in South Asia (VDSA) raw data files in excel format. The resulting output will be two data files in stata format, one at plot level and other at household level.  These plot level and household level data sets are also included in this repository.   The word file ‘guidelines’ contain instructions to extract VDSA raw data from VDSA knowledge bank and use them as inputs to run the STATA do file ‘VDSA data management for agricultural performance’   The VDSA raw data files in excel format needed to run the stata do file are also available in this repository for users convenience The raw VDSA data were generated by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in partnership with Indian Council of Agricultural Research (ICAR) Institutes and the International Rice Research Institute (IRRI) and funded by the Bill & Melinda Gates Foundation (BMGF) (Grant ID: 51937).  The data were acquired in surveys by resident field investigators. Data collection was mostly through paper based questionnaires and Samsung tablets were also used since 2012. The survey instruments used for different modules are available at <a href="http://vdsa.icrisat.ac.in/vdsa-questionaires.aspx">http://vdsa.icrisat.ac.in/vdsa-questionaires.aspx</a> Study sites were selected using a stepwise purposive sampling covering agro-ecological diversity of the region. Three districts within each zone were selected based on soil, climate parameters as well as the share of agricultural land under ICRISAT mandate crops. On similar lines, one typical sub-district within each district and two villages within each sub-district were selected. Within each village, ten random households from four landholding groups were selected. Selected farmers were visited by well trained, agriculture graduate, resident field investigators, once every three weeks to collect information related to various socioeconomic indicators. Some of the data modules like details on crop cultivation activities including plot wise input, output was collected every three weeks while others like general endowments were collected once at the beginning of every agricultural year.  The compiled data, source data, data descriptions and data management code are all published in a public repository at <a href="http://dataverse.icrisat.org/dataverse/socialscience">http://dataverse.icrisat.org/dataverse/socialscience</a> at  <a href="https://doi.org/10.21421/D2/HDEUKU">https://doi.org/10.21421/D2/HDEUKU</a>] Some of the several benefits of these data are: <ul> <li>Scientists, students, development practitioners can benefit from these data to track changes in the livelihood options of the rural poor as this data provides long-term, multi-generational perspective on agricultural, social and economic change in rural livelihoods.</li> <li>The survey sites provide a socio-economic field laboratory for teaching and training students and researchers</li> <li>This dataset can be used for diverse agricultural, development and socio-economic analysis and to better understand the dynamics of Indian agriculture.</li> <li>The data helps to provide feedback for designing policy interventions, setting research priorities and refining technologies.</li> <li>Shed light on the pathways in which new technologies, policies, and programs impact poverty, village economies, and societies</li> </ul>

Fig. 2 in Comparative analysis of cyclotide-producing plant cell suspensions presents opportunities for cyclotide plant molecular farming

Fig. 2. Cyclotide profiles in O. affinis cultures. A Relative quantification by MALDI-TOF-MS of cyclotides from in vitro grown organs, calli, and suspension cells of O. affinis. B Amino acid alignment of partial coding sequences encoding cyO2 derived from transcriptome sequencing of H. enneaspermus and a Sanger sequence-validated O. affinis transcript. Amino acid differences only exist within the N-terminal repeat (NTR) and C-terminal propeptide (CTPP) regions, whilst the coding sequence but not the peptide sequence differs between the two species. C Comparison of previously described kalata B18 (kB18) (Seydel et al., 2007) and Sanger sequence validated kalata B23 (kB23) presented in this work.

Fig. 3 in Comparative analysis of cyclotide-producing plant cell suspensions presents opportunities for cyclotide plant molecular farming

Fig. 3. Relative quantification by MALDI-TOF-MS of cyclotides from in vitro grown organs, calli, and suspension cells of A C. ternatea and B H. enneaspermus. Relative quantification was achieved by comparing the MS signal ratio between peptides of interest to that of an internally spiked peptide control using MALDITOF-MS.

Fig. 1 in Comparative analysis of cyclotide-producing plant cell suspensions presents opportunities for cyclotide plant molecular farming

Fig. 1. Example calli grown on optimal media formulations of A C. ternatea and B H. enneaspermus. Growth curves of suspension cultures (n = 3 for each media formulation) grown in media formulations for C C. ternatea and D H. enneaspermus. Growth was recorded relative to starting packed cell volume.

Fig. 5 in Comparative analysis of cyclotide-producing plant cell suspensions presents opportunities for cyclotide plant molecular farming

Fig. 5. Assessment of transcript or peptide levels present in O. affinis leaf, calli, and suspension cells at days 1, 14, 21 and 28. A Relative transcript expression determined by qPCR (n = 4) of asparaginyl endopeptidases OaAEP1, OaAEP2 and OaAEP3. B Relative transcript expression determined by qPCR (n = 4) of select cyclotide precursors Oak1 encoding kB1, Oak4 encoding kB2, and OaCyO2 encoding cyO2. C Relative peptide levels determined by MALDI-TOFMS (n = 4) of O. affinis cyclotides.

Fig. 6. A in Comparative analysis of cyclotide-producing plant cell suspensions presents opportunities for cyclotide plant molecular farming

Fig. 6. A Structural equivalence of cyO2 derived and purified from plant suspension cells or whole plant tissue as determined by NMR spectroscopy. B Cytotoxicity of cyclotides cyO2 and kB1 against Sf9 cells.

Fig. 4 in Comparative analysis of cyclotide-producing plant cell suspensions presents opportunities for cyclotide plant molecular farming

Fig. 4. Inducibility of cyclotide accumulation in O. affinis suspension cells. A Cyclotide accumulation profiles in O. affinis suspension cells exposed to biotic (methyl jasmonate, MeJA) and abiotic (NaCl) signals. Replicate (n =3) cultures were assayed 1, 4 and 7 days after addition of inducer. Treatments highlighted by an asterisk are significantly different (P <0.05). Cell morphology of B day 1 cells in 3 mM NaCl, C day 7 cells in 3 mM NaCl and D day 7 cells in 50 mM NaCl.

opencc-zeroAug 2023View details →

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Data from: Hierarchical power control of a large-scale wind farm by using a data-driven optimization method

With the participation in automatic generation control (AGC), a large-scale wind farm should distribute the real-time AGC signal to numerous wind turbines (WTs). This easily leads to an expensive computation for a high-quality dispatch scheme, especially considering the wake effect among WTs. To address this problem, a hierarchical power control (HPC) is constructed based on the geographical layout and electrical connection of all the WTs. Firstly, the real-time AGC signal of the whole wind farm is distributed to multiple decoupled groups in proportion of their regulation capacities. Secondly, the AGC signal of each group is distributed to multiple WTs via the data-driven surrogate-assisted optimization, which can dramatically reduce the computation time with a small number of time-consuming objective evaluations. Besides, a high-quality dispatch scheme can be acquired by the efficient local search based on the dynamic surrogate. The effectiveness of the proposed technique is thoroughly verified with different AGC signals under different wind speeds and directions.

opencc-by-4.0Jun 2021View details →

Data set used in article: Model Predictive Control for Wake Redirection in Wind Farms: a Koopman Dynamic Mode Decomposition Approach

Step-wise yaw deflection in 2 wind turbines in SOWFA. More information in the article.