Purpose

The aim of the conference is to consolidate interdisciplinary knowledge in the fields of agriculture, biology, robotics, information technology and economics to solve urgent problems of digitalization of organic livestock and crop production based on the achievements of fundamental science and the best practices of agricultural companies.

Topics

Digital technologies and automation in crop production
Digital technologies and automation in animal husbandry
Digital technologies and automation in aquaculture
Digital technologies and sustainable development of agriculture
Digital technologies, production and markets of organic products
Organic agriculture: organizational, economic and legal aspects
Rational nature management and ecology in agricultural production
Robotics in agriculture

Keynote Speakers

Natalia Zinovieva

Director of the Federal Research Center for Animal Husbandry named after Academy Member L.K. Ernst, Doctor of Biological Sciences, Professor, Academician of the Russian Academy of Sciences.

Podolsk, Russia

Lecture Title:

Genetic Resources of Farm Animals as the Foundation of Livestock Sustainability: Modern Methods of Conservation, Study, and Use

Abstract:

The orientation of agricultural production systems has led to a dramatic decline in the number of local breeds of agricultural animals, both in Russia and worldwide. Given that local genetic resources are carriers of unique forms of variability, their loss may lead to a reduction in genetic diversity, the preservation of which is the foundation for the sustainability of agricultural production systems in the future. The report will present the results of an analysis of the current state and global experience in preserving animal genetic resources ex situ in order to identify scientific and methodological approaches to preserving the domestic breed gene pool.

A brief overview of the breed diversity of agricultural animals will be provided based on data presented by the Food and Agriculture Organization (FAO), and the criteria for categorizing breeds according to their risk status, proposed by the FAO, will be presented. The advantages and disadvantages of two approaches to preserving animal genetic resources are discussed – in situ, i.e., within the conditions of agricultural production, and ex situ, i.e., outside the production systems for which the resource was created, including two forms – in vivo (“live” breeding) and in vitro (cryobanks or gene banks). The role of the National Center for Genetic Resources of Agricultural Animals, established on the basis of the Federal Research Center for Animal Husbandry – VIZH named after Academician L.K. Ernst in accordance with Presidential Decree No. 195 of March 19, 2024, as a key element of the scientific infrastructure in the system for preserving agricultural animal resources in the Russian Federation, will be demonstrated. The advantages and disadvantages of maintaining various types of genetic material in cryobanks will be discussed.

Sergey Sanin

Chief Researcher and Head of Department at the All-Russian Research Institute of Phytopathology, Academician of the Russian Academy of Sciences, Doctor of Biological Sciences, Professor.

Odintsovo District, Moscow Region, Russia

Tagir Ibragimov

Leading Researcher at the All-Russian Research Institute of Phytopathology, Candidate of Biological Sciences

Odintsovo District, Moscow Region, Russia

Lecture Title:

Deep Machine Learning Methods in Predicting Wheat Diseases

Abstract:

The main leaf diseases of winter wheat (septoria, rust diseases, powdery mildew) annually cause crop losses of 15-40%, depending on the region and weather conditions. A timely and accurate forecast of their development is the basis for optimizing protective measures and reducing the pesticide burden on agrocenoses. Traditional methods of phytopathological forecasting are based on empirical values of meteorological parameters.

However, these approaches have a number of limitations: they do not take into account nonlinear interactions between environmental factors (for example, temperature × humidity × phenophase); they ignore the temporal dynamics of pathogen development (incubation periods, cumulative effects); they use static thresholds. With the development of computer technologies, data collection, processing, and analysis have shifted towards the development of machine learning approaches. The developed ensemble systems for predicting the development of winter wheat diseases are presented. These systems use an artificial long short‑term memory (LSTM) neural network, a fully connected neural network (FCNN), and SHAP interpretation to ensure model transparency.

Alexey Evgrafov

Associate Professor, Department of Tractors and Automobiles, Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, Doctor of Technical Sciences.

Moscow, Russia

Lecture Title:

Digital Twins and Real-Time Forecasting of Climatic Conditions as a Basis for Climate Adaptation in Agriculture

Abstract:

Agriculture, and in particular crop production, directly depends on natural factors such as temperature, humidity, and precipitation. Recently, an increase in global temperature has been observed. According to studies by numerous authors, climate changes in cycles – short (7–11 years), medium (20–45 years), and century-scale (60–90 years).

It should be noted that in 2024 the average global temperature rose to 1.5 degrees Celsius. The main causes of this effect are greenhouse gas emissions generated by the burning of hydrocarbons and an increase in the number of natural fires, in particular peat and forest fires. Therefore, the development and application of digital twins of climatic conditions is currently relevant, as they model these scenarios and offer optimal solutions for adjusting technological operations in crop cultivation in real time.

Larisa Ilyina

Professor of the Department of Large Livestock Husbandry, Saint Petersburg State Agrarian University; Head of the Molecular Genetics Laboratory, OOO «BIOTROF», Doctor of Biological Sciences.

Saint Petersburg, Russia

Lecture Title:

Microbiome and Gene Expression as Key Factors in the Resistance of Agricultural Animals to Xenobiotics: Problems and Solutions

Abstract:

Contamination of feed and the environment with xenobiotics (antibiotics, toxins, pesticides) remains one of the most pressing problems in modern animal husbandry. Exposure to these substances negatively affects the health, productivity, and reproductive function of animals, causing disruptions in the digestive system, immune status, and metabolism. In recent years, it has been established that the intestinal microbiota plays a central role in ensuring the body’s resistance to toxicants.

Microorganisms not only participate in the direct biotransformation of harmful compounds, but also regulate the activity of the animal’s own defense systems through modulation of the expression of genes responsible for detoxification, antioxidant protection, and the immune response. The report will discuss current concepts of the mechanisms of interaction between xenobiotics (antibiotics, toxins, glyphosate) and the microbiome and host genome, as well as promising approaches to increasing animal resistance based on targeted correction of the microbial community. Particular attention is paid to the results of studies demonstrating the effectiveness of probiotic preparations and sorbents in neutralizing the toxic effects of these substances. It is shown that the combined use of these agents makes it possible both to reduce the entry of toxins into the body and to activate endogenous defense systems. The materials of the report are based on data from molecular genetic monitoring of the microbiome and gene expression in agricultural animals obtained during many years of research. The presented results can serve as a scientific basis for the development of new strategies for the prevention and correction of disorders caused by exposure to antibiotics, toxins, and glyphosate in industrial animal husbandry.

Valery Koshelev

Head of the Department of Management, Institute of Economics and Management of the Agro-Industrial Complex, Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, Doctor of Economics, Professor.

Moscow, Russia

Arina Frolova

Assistant, Department of Management, Institute of Economics and Management of the Agro-Industrial Complex, Russian State Agrarian University – Moscow Timiryazev Agricultural Academy

Moscow, Russia

Lecture Title:

Features of Evaluating Investment Projects for the Production of Wild-Growing Organic Products in the Arctic Zone of Russia

Abstract:

According to the Research Institute of Organic Agriculture (FiBL), the area of certified land in Russia used for the collection of food forest resources amounts to 801.7 thousand hectares (2.7% of the global area for organic wild plant collection). Considering that the area of forests, bogs, and meadows is approaching 1 billion hectares, and that 7.4–8.5 million tons of wild-growing plants could be harvested annually, Russia has the greatest potential in the world. However, according to expert estimates, only 6% of this volume is currently being collected.

The Arctic zone, rich in valuable wild-growing plants, is regarded as one of the priority regions for investment in the development of their harvesting and processing. However, when analyzing and evaluating investment projects in the northern territories, a number of specific conditions must be taken into account. For example, certification does not require any significant investment to meet organic standards, owing to the natural purity of the biocenoses and limited anthropogenic pressure. Nevertheless, it is necessary to account for logistical risks associated with the remoteness of the areas and the underdevelopment of transport systems.