The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one.

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.

The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities.

This brochure presents the five-year TAP-AIS project (2019-2024) funded by the European Union under the DeSIRA Initiative and implemented by the Food and Agriculture Organization (FAO) of the United Nations. The project has the main objective to strengthen capacities to innovate in national agricultural innovation systems (AIS) in the context of climate-relevant, productive, and sustainable transformation of agriculture and food systems in Africa, Latin America, Asia and the Pacific.

CABI and the Cereal Growers Association (CGA) have been sharing information with farmers in Kenya on how to effectively and safely manage the continuing threat of the invasive fall armyworm (Spodoptera frugiperda). This was achieved thanks to a  development communication campaign that combined video sharing through a network of lead farmers and social media.

To determine whether a farmer’s accessibility predicts the delivery of extension services, this study used banana Xanthomonas wilt (BXW) disease-management advisory as a typical case with which to collect extension-delivery information from 690 farmers, distinguished by their respective accessibility. Cost–distance analysis was applied to define each farmer’s accessibility. The results revealed that a farmer’s accessibility does not predict extension delivery to that farmer in all forms of the examined extension parameters.