Modern agriculture and food production systems are facingincreasing pressures from climate change, land and wateravailability, and, more recently, a pandemic. These factors arethreatening the environmental and economic sustainability ofcurrent and future food supply systems. Scientific andtechnological innovations are needed more than ever to secureenough food for a fast-growing global population. Scientificadvances have led to a better understanding of how variouscomponents of the agricultural system interact, from the cell tothe field level.
This paper addresses how co-producing knowledge can assist local farmers in reshaping their territories into sustainable farming systems. We describe the emergence and consolidation of an agroforestry system in an Eastern Amazon forest frontier, unpacking the co-production of a new farming system over recent decades. Instead of assuming pre-defined categories (e.g., traditional/technical, local/external), the analysis focuses on interactions among knowledge holders and how multiple knowledge sources are intercalated.
The future of inclusive forestry in Nepal depends on forestry professionals who can recognise patriarchal roots of gender injustice as they operate in the ideologies and apparatus of forest governance, and who can resist those injustices through their work. This paper uses the notion of knowledge practices to explore the recognition of injustice amongst Nepal’s community forestry professionals, and the relationship between recognition and resistance, highlighting the inherently political nature of all knowledge practices.
This article provides an overview of some of the recent research in agriculture involving remote sensing and GIS. Attention focuses on application of remote sensing and GIS specially in agriculture including geography, land surveying, most Earth Science disciplines, parent child relationship, unique identification, attributes, technical parameters, 2D/3D view and any other requirement customized. These advances have been made over recent years and foundations for future research established and can be efficiently used in Agriculture for better results.
This article examines how research on the agriculture and agrifood systems mobilizes the concept of Innovation System (IS). A literature review on the IS provides an analytical framework for determining its theoretical frame of reference, its area of application and its uses.
Classical innovation adoption models implicitly assume homogenous information flow across farmers, which is often not realistic. As a result, selection bias in adoption parameters may occur. We focus on tissue culture (TC) banana technology that was introduced in Kenya more than 10 years ago. Up till now, adoption rates have remained relatively low.
Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of themost challenging problems in agriculture. If the diseases are not identified in the early stages, then they may ad-versely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many re-searchers have presented different state-of-the-art systems based on Deep Learning and Machine Learningapproaches. However, most of these systems either use millions of training parameters or have low classificationaccuracies.
Many of the world’s food-insecure and undernourished people are smallholder farmers in developing countries. This is especially true in Africa. There is an urgent need to make smallholder agriculture and food systems more nutrition-sensitive. African farm households are known to consume a sizeable part of what they produce at home. Less is known about how much subsistence agriculture actually contributes to household diets, and how this contribution changes seasonally. We use representative data from rural Ethiopia covering every month of one full year to address this knowledge gap.
The recent proliferation of mobile phones in rural Africa has also led to increased interest in mobile financial services (MFS), such as mobile money and mobile banking. Such services are often portrayed as promising tools to improve agricultural finance, especially among smallholders who are typically underserved by traditional banks. However, empirical evidence on the actual use of MFS for agricultural activities is thin. Here, we use nationally representative data from Kenya to analyze the use of mobile payments, mobile savings, and mobile credit among the farming population.
Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually.