GreenDairy

Motivation

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

Goals and approach

GreenDairy aims to develop innovative animal-plant agricultural systems that are both ecologically and economically sustainable, as well as enabling a high degree of animal welfare and thus gaining a high level of acceptance in society.

Mixed dairy farming is considered to be one of the possible solutions for closing the decoupled material cycles. However, there is a lack of knowledge about the ecological, economic and animal welfare effects of different intensity levels of such production systems. 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 digitalized dairy farming system established in 2022, the organically managed Gladbacherhof farm at Justus Liebig University Giessen. This system enables the 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 maize silage and grain.

A total of five project areas form the basis of the project. Project area A (animal) deals with the effects of feeding intensity on performance, animal physiology, animal health and animal welfare, as well as their interactions. Project area B (plant) looks at selected characteristics of system-specific crop rotations and crop types as well as the different nutrient return from the barn to the field. In addition, optimization possibilities are 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 is being investigated in project area C (Environment). The aim of project area D (integrated system analysis) is the consideration and balancing of ecological, economic and social indicators for a comprehensive sustainability assessment. Important knowledge and recommendations for action for consulting and practice are derived from this. 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-based recording of agronomically relevant properties of legume/grass mixtures" in sub-project B1. Based on existing remote sensing models, the spatio-temporal variability of key agronomic performance parameters (yield, quality, crop composition) of legume-grass mixtures will be recorded. These parameters will be used to estimate the nitrogen-fixing capacity of legumes and their small-scale variability and can thus enable ways to avoid N emissions and make more efficient use of the farm's own manure. The remote sensing of legume-grass stands is carried out using sensors on commercially available drones. The sensor systems used are as application-oriented 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 the parameters and provide practical information;

• Data integration and analysis to identify and quantify soil-borne causes for the spatial variation of the performance parameters of legume-grass mixtures and the spatial variation of yield-affecting factors in the subsequent crop.