Sustainability of conventional agricultural production systems is threatened by triple challenges of energy and environmental crises, deteriorating natural resources bases, and declining farm profitability. Current agricultural management practices (energy-intensive, inefficient external and natural input use, and crop biomass burning) are negatively impacting the ecosystem services which are the principal drivers for food security and human survival. Hence, there is a need to shift from unsustainable production practices to cleaner production systems. Energy use, carbon footprint (CF), and economic sustainability are important indicators of any clean production systems. Thus, a hypothesis was formulated that no-till (NT) cultivation along with mulching can provide an environmentally clean crop production practice that can enhance energy use efficiency, economic profitability, and reduce the CF. Therefore, the present experiment was conducted for four consecutive years (2012–15) to evaluate the energy budget, CF, and economics of NT along with bio-mulching for a cleaner upland rice production system. The experiment comprised of two tillage practices i.e., conventional tillage (CT) and NT in main plots and four bio- mulches in subplots i.e., rice straw mulch (RSM), Gliricidia sepium mulch (GLM), brown manuring mulch (BMM) of cowpea (Vigna unguiculata), and no mulch (NM) as a control. Results revealed that the adoption of NT curtailed energy use by 48.50%, specific energy by 49.63%, CF by 16.48%, and cost of cultivation by 35% in addition to enhancing energy use efficiency and benefit to cost ratio in comparison to CT. It was also observed that mulching, particularly the BMM, boosted the energy use efficiency, economic productivity, net returns, and benefit to cost ratio over NM. The results suggested that NT with BMM is an environmentally clean production technology to enhance the energy use efficiency, besides reducing the CF of direct-seeded upland rice production system in the Eastern Himalayas and similar eco-regions of the world.
Tag: agricultural robots
The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems
Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams. Digital technology use in agriculture and agrifood systems research accelerates the production of multidisciplinary data, which spans genetics, environment, agroecology, biology, and socio-economics. Quality labeling of data secures its online findability, reusability, interoperability, and reliable interpretation, through controlled vocabularies organized into meaningful and computer-readable knowledge domains called ontologies. There is currently no full set of recommended ontologies for agricultural research, so data scientists, data managers, and database developers struggle to find validated terminology. The Ontologies Community of Practice of the CGIAR Platform for Big Data in Agriculture harnesses international expertise in knowledge representation and ontology development to produce missing ontologies, identifies best practices, and guides data labeling by teams managing multidisciplinary information platforms to release the FAIR data underpinning the evidence of research impact. The deployment of digital technology in Agriculture and Food Science accelerates the production of large quantities of multidisciplinary data. The Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture harnesses the international ontology expertise that can guide teams managing multidisciplinary agricultural information platforms to increase the data interoperability and reusability. The CoP develops and promotes ontologies to support quality data labeling across domains, e.g., Agronomy Ontology, Crop Ontology, Environment Ontology, Plant Ontology, and Socio-Economic Ontology.