L’herbe pâturée est l’aliment qui coûte le moins cher dans une ration et la bonne gestion de l’herbe passe entre autre par une connaissance des quantités disponibles. Afin de simplifier et d’automatiser ces mesures d’herbe, et ainsi contribuer au maintien voire au développement du pâturage, le projet HERDECT s’est attaché à construire des méthodes d’estimation de la biomasse des prairies à partir d’outils de télédétection (d’acquisition à distance) et à en estimer la faisabilité opérationnelle.
Women in agriculture are far from the end of poverty, zero hunger, quality education, and gender equality — some of the sustainable development goals that can be significantly improved if we achieve greater participation and better working conditions for women in agriculture.
The 2021 Global Report on Food Crises (GRFC 2021) highlights the remarkably high severity and numbers of people in Crisis or worse (IPC/CH Phase 3 or above) or equivalent in 55 countries/territories, driven by persistent conflict, pre-existing and COVID-19-related economic shocks, and weather extremes. The number identified in the 2021 edition is the highest in the report’s five-year existence. The report is produced by the Global Network against Food Crises (which includes WFP), an international alliance working to address the root causes of extreme hunger.
Les technologies agricoles jouent un rôle central dans la production alimentaire mondiale. Une étude récemment publiée par le Conseil des technologies de l’information et des communications (CTIC) s’est penchée sur la question. On y fait valoir que les technologies sont nécessaires à l’amélioration de l’efficacité de la production agricole et de la durabilité des denrées alimentaires.
The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse.
This document is part of the project “Strengthening the adaptive capacity to climate change in the fisheries and aquaculture sector of Chile”, executed by the Undersecretariat of Fisheries and Aquaculture and the Ministry of the Environment, and implemented by the Food and Agriculture Organization of the United Nations, with funding from the Global Environment Facility. The work was implemented in four pilot coves: Caleta Riquelme (Tarapacá); Caleta Tongoy (Coquimbo); Caleta Coliumo (Biobío); and Caleta El Manzano-Hualaihué (Los Lagos).
Esta obra está enmarcada en el Plan de Acción 2018-2021 de la Facultad de Ciencias Agrarias, en el reto “Aportar al Sistema Nacional de Innovación Agropecuaria integrando la investigación y la extensión”, el cual tiene como propósito interconectar la investigación, la extensión y la innovación para mejorar el relacionamiento con el sector productivo, fortalecer las alianzas público-privadas nacionales e internacionales y las redes del conocimiento y gestionar la innovación, por medio de la creación del Centro de Innovación Agropecuaria, con el propósito de mejorar los procesos de gestión de
The national assessment of the agricultural innovation system (AIS) in Malawi was conducted using a framework of four types of analyses: functional, structural, capacity and enabling environment analysis. The approach included five case studies that addressed three methods including the use of indigenous methods for fall armyworm (FAW) control in Farmer Field Schools (FFS), livestock transfer programs, and a horticulture marketing innovation platform in Mzimba, Ntchisi, Balaka, and Thyolo districts.
The impact of global warming on crop growth periods and yields has been evaluated by using crop models, which need to provide various kinds of input datasets and estimate numerous parameters before simulation. Direct studies on the changes of climatic factors on the observed crop growth and yield could provide a more simple and intuitive way for assessing the impact of climate change on crop production.
Soil texture is a key soil property influencing many agronomic practices including fertilization and liming. Therefore, an accurate estimation of soil texture is essential for adopting sustainable soil management practices. In this study, we used different machine learning algorithms trained on vis–NIR spectra from existing soil spectral libraries (ICRAF and LUCAS) to predict soil textural fractions (sand–silt–clay %). In addition, we predicted the soil textural groups (G1: Fine, G2: Medium, and G3: Coarse) using routine chemical characteristics as auxiliary.