Stimulating Innovation for Global Monitoring of Agriculture (SIGMA) and its Impact on the Environment in support of GEOGLAM (SIGMA)

Global population has increased from about 2.5 billion in 1950 to more than 7.5 billion in 2017 and is projected to reach more than 9 billion by 2050. According to FAO, to achieve food for all, global food production will need to grow by 70% and up to 100% in developing countries. Sustainable intensification of agriculture is thereby imperative, requiring a thorough understanding of the impact of shifting cultivation practices on the environment. In this perspective, earth observation based information systems, which are currently mostly focused on short term agricultural productivity forecasts, will need to be enhanced with the capacity to assess the dynamics of cultivation practises and their impact on productivity and the environment. This is a key requirement to explore possible pathways towards sustainable agriculture in the long term.

SIGMA is part of Europe’s contribution to GEOGLAM, actively networking expert organizations world-wide, in a common effort to enhance current remote sensing based agricultural monitoring techniques. SIGMA is financed through the EC’s Research Framework programme (FP7), and it is implemented from local to global level in a wide partnership of 22 partners by establishing operational test site networks in Europe, Russia, Ukraine, China, Vietnam, Africa, Argentina and Brazil, under the leadership of VITO.

SIGMA develops remote sensing based methods to identify, map and assess:

  • Agriculture and crop land changes, globally, regionally and locally
  • Changes in agricultural production levels and shifts in cultivation practices
  • Environmental impacts of Agriculture and cultivation practices

Activities are undertaken at global, regional and local level, the main challenge being to thoroughly understand the dynamics, interactions and validity of the developed methods at various scales.


SIGMA aims to establish an operational network of globally distributed research and monitoring organizations in Europe, Asia, Africa, and South America. Its research activities are strengthening international agricultural risk management capacity. Developed methods will significantly increase scientific knowledge and understanding of agricultural dynamics in relation to the environment and produce tangible products:

  • crop land maps and statistics, identifying potential for expansion
  • maps of agricultural systems, shifts in cultivation practises and crop yield gaps, identifying potential for intensification
  • assessments of impact of agriculture on the environment, both due to intensification as expansion
  • training sessions, modules and materials on remote sensing based agricultural monitoring


The major outcomes of FAO, in collaboration with work-package partners, are related to:

  1. Data gap Analysis and Global agro-environmental Stratification;
  2. Land cover and crop land statistics; and
  3. Technology Transfer and Capacity Development



FAO is partner of the “SIGMA” global partnership programme in agricultural monitoring, with a strong involvement in GEO and the Global Agricultural Geo-Monitoring (GEO-GLAM) initiative. FAO contributes to SIGMA by means of its considerable experience in working with countries and their suite of national projects, strong linkages with local communities and capacity development proficiency, to increase “Agro-environmental” monitoring capacity and awareness at the global, regional and local level, to identify data and knowledge gaps, and to develop innovative methods and indicators to monitor and assess progress towards “sustainable agriculture”, focused on the assessment of longer term impact of agricultural dynamics on the environment and vice versa.

FAO and in collaboration with WP lead and WP3.1 partners has contributed to the development of a Global Agro Environmental Stratification database which is a key deliverable contextualized in the SIGMA project. The new global map of agro-environmental strata is based on improved zonation of both agro-ecological and socioeconomic factors which creates the basis for better global monitoring of the agricultural production. In addition a further characterisation and description of the GAES was coordinated by FAO populating the system with all relevant existing data sets for the spatial stratification and generation of a global map of agro-environmental zones taking into account the landscape patterns, overall climatic zones, cropping systems, main production areas, phenology, field size distribution and, crops calendar.

The GAES will benefit international organizations and Countries by providing an extensive database to support decision-makers in agriculture, natural resource management and food security. GAES is also targeting research experts in the field of Ecosystem and Ecosystem Services providing a framework for robust environmental sampling at global scale.

FAO, as part of its SIGMA activities, developed a Crop Land Nomenclature, in line with its Land Cover Meta Language (LCML) based on clear and quantifiable physiognomic/structural aspects of planted crops. The nomenclature is meant to serve as a reference to as many as possible, remote sensing based, categorizations of plant agriculture.

Under the WP33, crop land and change maps and statistics, innovative methodologies and tools identifying potential for expansion are developed under the FAO leadership for two selected countries: Kenya and Pakistan.

FAO has dedicated a particular attention to strengthen the capacities of agencies concerned with the collection, processing, consolidation, and use of geospatial data, in providing timely and precise agricultural statistics based on in-depth interpretation of survey and satellite data.

Under the SIGMA WP6 - FAO leadership - and based on the capacity development needs assessment and strategy developed by FAO, three curriculums/modules and lessons and learning materials on remote sensing based agricultural monitoring are developed:

- MODULE 1: Methodological aspects of the use of geospatial technology for agriculture statistics

- MODULE 2: Improved Agricultural Monitoring using RS; and

- MODULE 3: Geospatial data for Monitoring Agricultural changes and environmental impacts

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