This paper shared lessons learnt from the project of Improving Productivity and Market Success in forage development approaches, scaling up strategies, opportunities and challenges in the process of farmer innovations and innovative interventions in the value-chain of market oriented livestock development in relation to sustainable use of natural resources in two districts in Ethiopia.
There is a growing concern by governments, retailers and consumers about the safety and quality of food. Because products are resourced on a on a global scale it becomes important that the origin of the products as well as all the treatments during production can be traced and that the production methods can be verified as good agricultural practices (GAP). This also includes considerations for the environment and sustainability.
Ethiopia has a diverse agro-ecology and sufficient surface and ground water resources, suitable for growing various temperate and tropical fruits. Although various tropical and temperate fruits are grown in the lowland/midland and highland agro-ecologies, the area coverage is very limited. For example, banana export increased from less than 5,000 tons in 1961 to 60,000 tons in 1972, but in 2003 declined to about 1,300 tons worth less than USD 350,000.
Graduate programs in agriculture and allied disciplines in Ethiopia are expected to make concrete contribution to market-oriented development of smallholder agriculture. This, among others, calls for realignment and engagement of the programs with smallholder farmers and, value chain, R&D and policy actors. No panacea exists, however, as to how to ensure effective linkages, and thereby responsiveness. Lessons from initiatives on the ground in the country and beyond is thus crucial to inform the development of appropriate policy and innovative strategy.
Ethiopian needs to achieve accelerated agricultural development along a sustainable commercialization path to alleviate poverty and ensure overall national development. In this regard, sustainable commercial of smallholder dairying provides a viable and growing opportunity; with deliberate, appropriate and sustained policy support. A recent empirical analysis concludes however, that Ethiopian smallholder dairy sub-sector has not been able to take-off despite decades of development interventions.
The problems of agricultural development for small and medium enterprises (SMEs) are considered. The features of modeling business processes in agriculture are analyzed. A financial decision support system is proposed to increase sustainability and reduce risks in the development of agricultural SMEs. The software modules are based on TEO-INVEST.
For an intelligent agricultural robot to reliably operate on a large-scale farm, it is crucial to accurately estimate its pose. In large outdoor environments, 3D LiDAR is a preferred sensor. Urban and agricultural scenarios are characteristically different, where the latter contains many poorly defined objects such as grass and trees with leaves that will generate noisy sensor signals. While state-of-the-art methods of state estimation using LiDAR, such as LiDAR odometry and mapping (LOAM), work well in urban scenarios, they will fail in the agricultural domain.
It is difficult to establish the precise mathematical model of agricultural wheeled robots with differential drive for path tracking control, due to characteristics of nonlinear, strong coupling and multivariable. Here, path tracking control is studied for agricultural wheeled robot with differential drive based on sliding mode variable structure. Firstly, the motion model of agricultural wheeled robots with differential drive is established and control goal is determined for path tracking. Then, sliding mode variable structure is applied to design the controller.
This paper presents Thorvald II, a modular, highly re-configurable, all-weather mobile robot intended for applications in the agricultural domain. Researchers working with mobile agricultural robots tend to work in a wide variety of environments such as open fields, greenhouses, and polytunnels. Until now agricultural robots have been designed to operate in only one type of environment, with no or limited possibilities for customization.
3D Move To See (3DMTS) is a mutli-perspective visual servoing method for unstructured and occluded environments, like that encountered in robotic crop harvesting. This paper presents a deep learning method, Deep-3DMTS for creating a single-perspective approach for 3DMTS through the use of a Convolutional Neural Network (CNN). The novel method is developed and validated via simulation against the standard 3DMTS approach.