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Integrated animal-plant agroecosystems.


Animal breeding and husbandry represent important cornerstones for German agriculture. However, industrialization and specialization in agriculture has led to structures characterized by decoupled material cycles with high nitrogen surpluses, greenhouse gas emissions, land competition, soil degradation and problems with animal welfare. Alternatives such as sustainable dairy farming systems in ecological mixed farms with the value chains milk, meat and plant-based food are called for.

Goals and approach

GreenDairy aims to develop innovative animal-plant-agriculture systems that are both ecologically and economically sustainable and allow for a special level of animal welfare, thus gaining high acceptance in society.

Dairy farming on mixed farms is considered one of the possible solutions to close the decoupled material cycles again. However, there is a lack of knowledge about the effects of different intensity levels of such production systems on ecology, economy and animal welfare. This knowledge gap is to be closed in an interdisciplinary research approach involving animal, plant, soil and environmental sciences as well as agricultural and food economics.

Innovations and perspectives

The project draws on the research infrastructure of a digitized dairy farming system established in 2022, the ecologically managed Gladbacherhof at Justus Liebig University Giessen. This system enables scientific comparison of high- and low-input dairy production systems with digital animal recording, grazing control, and feeding and milking robotics. Low-input systems with grazing and predominantly roughage from grassland have so far been considered the standard in organic dairy farms. Alternatively, in the high-input system with grazing, the animals are also fed a high proportion of the farm's own corn silage and grain.

A total of five project areas form the basis of the project. Project Area A (Animal) looks at the effects of feeding intensity on performance, animal physiology, health and welfare, and their interactions. Project area B (crop) looks at selected characteristics of system-specific crop rotations and crop types, as well as the different nutrient recycling from the barn to the field. In addition, optimization opportunities are being explored, particularly in the management of forage legumes and through the introduction of drought-tolerant crops and site-specific cropping systems. The environmental impact of the two feeding systems will be investigated in project area C (Environment). The aim of project area D (Integrated Systems Analysis) is to consider and balance ecological, economic and social indicators for a comprehensive sustainability assessment. From this, important knowledge and recommendations for action for consulting and practice are derived. The project area Z (coordination and management) is responsible for the organization and smooth running of the research network.

The Department of Grassland Science and Renewable Resources is active in project area B (Plant) and is investigating the "remote sensing of agronomically relevant properties of legume/grass mixtures" in subproject B1. Based on existing remote sensing models, the spatio-temporal variability of key agronomic performance parameters (yield, quality, stand composition) of legume-grass mixtures will be recorded. These parameters will be used to estimate the nitrogen fixation performance of legumes and their small-scale variability, and may thus provide ways to avoid N emissions and use the farm's own manure more efficiently. Remote sensing of legume-grass stands is performed using sensors on commercially available drones. Sensor systems are used that are as close to the application as possible and can be used in practice with the least possible effort.

The main tasks are:

  • Model development using drone-based spectral sensors to estimate agronomic performance parameters of legume-grass mixtures;
  • Generation of maps that visualize the spatial variation of parameters and provide practical information;
  • Data integration and analysis to identify and quantify soil-borne causes of spatial variation in performance parameters of legume-grass mixtures and spatial variation in factors affecting yield in the succeeding crop.