Organic Eprints
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Wo Biohöfe finanziellen Dünger finden
No full textMit Direktzahlungen allein floriert der Biolandbau kaum. Es lohnt sich, auch andere Geldquellen zu erschliessen. Eine Auswahl
A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification
Get PDFThe hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. A high leaf hair density present on the abaxial surface of the grapevine leaves influences their wettability by repelling forces, thus preventing pathogen attack such as downy mildew and anthracnose. Moreover, leaf hairs as a favorable habitat may considerably affect the abundance of biological control agents. The unavailability of accurate and efficient objective tools for quantifying leaf hair density makes the study intricate and challenging. Therefore, a validated high-throughput phenotyping tool was developed and established in order to detect and quantify leaf hair using images of single grapevine leaf discs and convolution neural networks (CNN). We trained modified ResNet CNNs with a minimalistic number of images to efficiently classify the area covered by leaf hairs. This approach achieved an overall model prediction accuracy of 95.41%. As final validation, 10,120 input images from a segregating F1 biparental population were used to evaluate the algorithm performance. ResNet CNN-based phenotypic results compared to ground truth data received by two experts revealed a strong correlation with R values of 0.98 and 0.92 and root-mean-square error values of 8.20% and 14.18%, indicating that the model performance is consistent with expert evaluations and outperforms the traditional manual rating. Additional validation between expert vs. non-expert on six varieties showed that non-experts contributed to over- and underestimation of the trait, with an absolute error of 0% to 30% and -5% to -60%, respectively. Furthermore, a panel of 16 novice evaluators produced significant bias on set of varieties. Our results provide clear evidence of the need for an objective and accurate tool to quantify leaf hairiness
Ökologische Landwirtschafts- und Lebensmittelsysteme als Modelle für nachhaltige Ernährungssysteme in Europa und Nordafrika (Verbundvorhaben)
No full textDas hier beschriebene Verbundvorhaben umfasst folgende Teilprojekte: FKZ 19OE153, FKZ 19OE154
Das Projekt Okologische Landwirtschafts- und -Lebensmittelsysteme als Modelle für nachhaltige Ernährungssysteme in Europa und Nordafrika (SysOrg) zielte darauf ab, Interventions- und Ansatzpunkte für eine Transformation der Lebensmittelsysteme hin zu widerstandsfähigen, nachhaltigen Lebensmittelsystemen zu identifizieren, um eine erfolgreiche Gestaltung von Wegen zur Steigerung des nachhaltigen Konsums und der Lebensmittelproduktion zu ermöglichen. Fünf Fallgebiete in Europa und Nordafrika wurden aus vier Perspektiven untersucht: Umstellung auf eine nachhaltige und gesunde Ernährung (Ernährungsperspektive), Förderung ökologischer Lebensmittel und des ökologischen Landbaus (Öko-Perspektive), Verringerung der Lebensmittelverschwendung (Abfall-Perspektive) und Status des Systemübergangs (Übergangsperspektive). In jedem Fallgebiet wurden Sekundärforschung, Haushaltsbefragungen und halbstrukturierte Interviews mit ausgewählten Initiativen, die in den Bereichen der SysOrg-Perspektiven aktiv sind, durchgeführt, um perspektivenorientierte und fallübergreifende Analysen zu ermöglichen. Eine Analyse auf Gebietsebene basierend auf den Ergebnissen einer Haushaltsbefragung auf Recall-Basis ergab eine insgesamt gesündere und nachhaltigere Ernährung in Kopenhagen und
Warschau, den höchsten selbstberichteten Anteil an Bio-Lebensmitteln in Kopenhagen und Nordhessen, eine Diskrepanz zwischen dem Niveau der Lebensmittelabfälle pro Haushalt und
pro Kopf (z. B. die höchsten pro Haushalt und die niedrigsten pro Kopf-Abfälle in Kenitra) und den Arten der vorherrschenden Lebensmittelabfälle (teilweise verwendete Lebensmittel in Warschau, Cilento, Kopenhagen, Essensreste in Kenitra und Essensreste nach der Lagerung in
Nordhessen). In allen Fallgebieten gibt es eine beträchtliche Anzahl von Initiativen, die sich an
der Umgestaltung ihres lokalen Lebensmittelsystems beteiligen, wobei die meisten von ihnen auf Nischenebene tätig sind
Vitiforestry – A sustainable strategy for viticulture to cope with climate change? Reduced arbuscular mycorrhizal fungi richness in salix tree roots in comparison to vine roots of an agroforestral vineyard system
No full textGlobal warming and extreme events such as droughts or floods are challenging agriculture leading to soil erosion and increased pest pressure. The implementation of trees in agricultural fields (agroforestry) is expected to minimize soil erosion, reduce pest pressure and increase microbial activity. The soil microbiome including arbuscular mycorrhizal fungi (AMF) play an important role in soil fertility by forming symbiotic associations with land plants and connecting trees with neighbouring crops, which profit from shared and facilitated nutrient and water uptake. The aim of this master thesis was to study the impact of salix trees on soil chemistry and AMF diversity, in a vineyard located in the Western part of Switzerland. Amplicon sequencing was used to identify different AMF orders in soil and root samples. First, looking at all sequences, we found that the relative abundance of AMF is significantly lower in tree roots compared to vine. Next, focusing on the AMF sequences, we found that root samples of tree and vines had a lower diversity than soil samples, while the richness was higher in vine roots compared to tree roots. An ordination analysis finally suggested that the AMF community composition of tree roots was more different to vine roots, than soil samples of trees are to vine roots. To summarize: salix trees did not have an impact on AMF diversity. Furthermore, the slope position or the field management had more impact on soil chemistry than the impact of trees, indicating that enhancing sustainability in vineyards may require measures beyond implementing agroforestry into a viticultural system
No broccoli without a head - Testing UV light traps and hemp mulch to control the swede midge (Diptera: Cecidomyiidae), an important pest of the Brassicaceae
No full textThe swede midge (Contarinia nasturtii, Diptera: Cecidomyiidae) is a detrimental pest on brassicaceous plants with no effective method established to control the pest in the field. In this study, we tested and evaluated UV light traps and hemp mulch mats as alternative methods to control the swede midge. In a lab setting, UV light traps recaptured 81% of released swede midges. In a six-week field trial with high pest pressure, UV light traps successfully trapped gall midges but did not reduce swede midge damage on broccoli plants when activated for three days a week. Adjustments to activation time and light colour could increase the efficacy of light traps in catching the swede midge. Further, light traps could be
combined with other management strategies to improve pest control in the field. In a lab setting, hemp mulch mats did not reduce swede midge emergence, despite limiting the area available for swede midge larvae to access soil for pupation. Our study presents the first results on using UV light traps to control the swede midge under high pest pressure and indicates swede midge larvae being more mobile than previously assumed, potentially finding and entering openings in mulching covers for pupation
Europe and European Union: Key facts and Figures
Get PDFThe latest global organic agriculture data for 2023 reveal steady progress. Organic farmland expanded to nearly 99 million hectares, with notable increases in Latin America, Europe, and Africa, while North America and Oceania reported slight declines. However, the number of organic producers decreased by over 4 percent, driven by reductions in India and Thailand. On the trade front, organic exports to the USA surged, while EU imports saw a decline. Despite economic pressures and challenges, global retail sales of organic products grew to over 136 billion euros
Hof- und Recyclingdünger im Biolandbau
No full textDas Merkblatt erläutert die geltenden Anforderungen von Bio Suisse für die Zufuhr und Abgabe von Hof- und Recyclingdüngern und zeigt auf, worauf es im Detail zu achten gilt. Betriebsbeispiele veranschaulichen die Anwendung in der Praxis
Organic Agricultural Knowledge and Innovation System (AKIS) in France
Get PDFFarmers can reach out to trusted providers of organic advisory services.
This practice abstract lists the types of AKIS actors with linked examples that farmers can turn to, in an order of ranking by French AKIS workshop attendees based on availability, competencies and affordability.
Practical recommendations:
1. Agricultural technical institutes: Arvalis, Terres Inovia, GAB, FNAB, ITAB, IFV
2. Chambers of Agriculture
3. Producer groups with a facilitator
4. Regional experimentation platforms: GRAB
5. Advisors and independent consultant: CETA
6. Digital platform: Triple Performance, GECO, R&D Agri, agroecologie.org
7. Social media
8. Start up or other independent organisation: Ver de terre production, fermes d’avenir, Solagro
9. Public and government players: Agence Bio, Community of municipalities
10. Cooperatives and producer groups
11. French agricultural research institute: INRAe
12. The agricultural education system
13. Certification bodies: INAO, Ecocert
14. Specialised press, information bulletins
15. Fellow farme
Biogemüsefibel 2025 - Infos aus Praxis, Beratung und Forschung rund um den Biogemüse- und Kartoffelbau
Get PDFDie Beiträge der Biogemüsefibel umfassen vielseitige Themen: von Züchtung, Anbau- und Sortenversuche, über torfreduzierter Anzucht bis hin zu organischer Nährstoffaufnahme. Beiträge zur Ressourcenschonung beim Anbau von Bio-Chicorée und mechanischen Methoden gegen den Kartoffelkäfer ergänzen das Spektrum. Ein besonderer Schwerpunkt liegt auf den Bionet-Versuchen, mit Ergebnissen zu Radicchio, Regulierungsmöglichkeiten zur Grünen Reiswanze, den Auswirkungen von Silofolie auf die Bodenbiodiversität und innovativen Ansätzen in der Agri-PV
und Gemüseproduktion
Comparing different statistical models for predicting greenhouse gas emissions, energy-, and nitrogen intensity
No full textTo evaluate the environmental impact across multiple dairy farms cost-effectively, the methodological framework for environmental assessments may be redefined. This article aims to assess the ability of various statistical tools to predict impact assessment made from a Life Cyle Assessment (LCA). The different models predicted estimates of Greenhouse Gas (GHG) emissions, Energy (E) and Nitrogen (N) intensity. The functional unit in the study was defined as 2.78 MJMM human-edible energy from milk and meat. This amount is equivalent to the edible energy in one kg of energy-corrected milk but includes energy from milk and meat. The GHG emissions (GWP100) were calculated as kg CO2-eq per number of FU delivered, E intensity as fossil and renewable energy used divided by number of FU delivered, and N intensity as kg N imported and produced divided by kg N delivered in milk or meat (kg N/kg N). These predictions were based on 24 independent variables describing farm characteristics, management, use of external inputs, and dairy herd characteristics.
All models were able to moderately estimate the results from the LCA calculations. However, their precision was low. Artificial Neural Network (ANN) was best for predicting GHG emissions on the test dataset, (RMSE = 0.50, R2 = 0.86), followed by Multiple Linear Regression (MLR) (RMSE = 0.68, R2 = 0.74). For E intensity, the Supported Vector Machine (SVM) model was performing best, (RMSE = 0.68, R2 = 0.73), followed by ANN (RMSE = 0.55, R2 = 0.71,) and Gradient Boosting Machine (GBM) (RMSE = 0.55, R2 = 0.71). For N intensity predictions the Multiple Linear Regression (MLR) (RMSE = 0.36, R2 = 0.89) and Lasso regression (RMSE = 0.36, R2 = 0.88), followed by the ANN (RMSE = 0.41, R2 = 0.86,). In this study, machine learning provided some benefits in prediction of GHG emission, over simpler models like Multiple Linear Regressions with backward selection. This benefit was limited for N and E intensity. The precision of predictions improved most when including the variables “fertiliser import nitrogen” (kg N/ha) and “proportion of milking cows” (number of dairy cows/number of all cattle) for predicting GHG emission across the different models. The inclusion of “fertiliser import nitrogen” was also important across the different models and prediction of E and N intensity