About

GEOGLAM RAPP

With global population predicted to reach around 9 billion by 2050, not only intensive croplands, but also rangelands, scrublands and pasturelands will continue to come under pressure to further increase their productivity, grassland plant biomass and increase animal protein production, to supply an ever-growing global need for of essential animal protein. At the same time there is much focus on making sure that they are also being managed in a sustainable manner into the future, as their overall productivity is enhanced.

Currently there is no comprehensive global effort for monitoring the status and productivity of pastures and rangelands. Therefore GEO, the Group on Earth Observations and it’s Global Agricultural Monitoring (GEOGLAM) initiative, propose to bring together space agencies, existing associated institutional frameworks, in-situ networks, and the pasture productivity modelling community, to establish a dedicated global system for observing pastures and rangeland status, and ultimately to also estimate biomass dynamics and productivity. The working definition of ‘rangelands’ used here, includes both small farms and extensive livestock operations covering grasslands, savannah woodlands and arid and semi-arid scrublands, where native plant species support production of livestock, while ‘pasturelands’ are defined as those lands, where improved plant species designed for livestock nutrition dominate the system, and where higher levels of inputs are used (nutrients, water, active stock management, etc.).

Termed “GEOGLAM Rangelands and Pasture Productivity (RAPP)”, this new GEO initiative will provide the global community the means to regularly monitor the world’s rangelands and pasture lands on a routine basis, and their capacity to produce animal protein in real-time, at global, regional and national levels. The primary scope of RAPP will therefore be to monitor those lands that are integral to producing animal protein on a ‘free-range’, open-field basis. Where technically possible and where national animal herd statistics are available, the system will include global and regional population information on beef cattle, goats, sheep, camels, pork, dairy cattle, wild and managed buffalo and deer. The system will not address at this point ocean fisheries, and fish-farms, intensive cattle yards, piggeries and chicken/duck farms, which depend on imported feed.

Building on other GEO capabilities and links to space agencies, RAPP will integrate earth observation and in situ data, with modelling approaches to map across the globe:

  • the dynamics of the nature and quantity of available plant biomass, including its condition and trends in productivity, as affected by natural and human-induced impacts across the globe; and
  • the nature and quality of the animals that feed on the biomass and their protein production.

GEOGLAM

The Group on Earth Observations (a partnership of governments and international organizations) developed the Global Agricultural Monitoring (GEOGLAM) initiative in response to the growing calls for improved agricultural information. The goal of GEOGLAM is to strengthen the international community’s capacity to produce and disseminate relevant, timely and accurate forecasts of agricultural production at national, regional and global scales through the use of Earth Observations (EO), which include satellite and ground-based observations. This initiative is designed to build on existing agricultural monitoring programs and initiatives at national, regional and global levels and to enhance and strengthen them through international networking, operationally focused research, and data/method sharing.

Both GEOGLAM and AMIS were endorsed by the G20 Heads of States’ Declaration (Cannes, November, 2011), when GEOGLAM was tasked to “coordinate satellite monitoring observation systems in different regions of the world in order to enhance crop production projections and weather forecasting data.”

GEOGLAM page on the GEO website

GEOGLAM Crop Monitor

Some pasture and rangeland food production facts

The world’s grazing lands:

  • Rangelands – native species often used for livestock
  • Pasturelands – improved species and designed for livestock
  • Largest land use system on earth

The importance of these systems and these landscapes:

  • Large proportion of the earth’s surface (48% of biomass is consumed by grazing animals)
  • Large and growing population with livelihoods dependent on sustained production
  • Food impact beyond the footprint
  • Greenhouse gas source (approx. 12%)
  • multiple roles in human nutrition and welfare and key foundations for many ecosystems and biodiversity – many in vulnerable states

Efficiency solutions are yet to be realised

The Challenge

  • Increased demand for animal protein
  • Increase in stocking rates to match demand
  • Pressure from cropping needs – and the need to preserve forests
  • Climate variability and climate change,
  • Increase in urban livestock production and need for feed sources
  • Increased vulnerability as systems intensify

Projected meat consumption trends, 1960s to 2030.

Food Demand, 1500 to 2010.

 

Specific Outcomes

The ultimate goal of GEOGLAM RAPP and the associated GEO task group that will establish the monitoring system, will be to develop a global monitoring capability and associated domain expertise, to track the amount and status of plant biomass and animal protein produced from open rangelands and managed pastures, and also importantly the status of the land systems that sustain this production into the future.

At a technical level, despite significant achievements in the measurement and monitoring of vegetation cover, tree cover, greenness and more recently dead cover, and in the detection and monitoring of tree cover, this has not translated into robust global operational systems that link to livestock production estimates/models, and move beyond regional coverage within some countries; there is no operational global assessment. Consequently, success will in the first instance be measured by the development of at least one system established to monitor and report on rangelands / pasture lands status and production across key regions of the globe.

Key outcomes from success of the task will include:

  • an improved capacity to manage risk and improve production of animal protein at a range of scales due to a better understanding of the trends in biomass and its use for protein production.
  • the capacity to more effectively manage variability in production due to more timely and accurate national and regional agricultural statistical reporting and early warning of meat production shortfalls.
  • more effective planning based on accurate forecasts of pasture and rangelands productivity variability.
  • improved global understanding of risk across all landscapes as climate and land use change through the addition of these lands into global agricultural monitoring.

Success in the task and the RAPP per se, will be analogous to related GEOGLAM components, however, for many reasons, the institutional and operational capacity in rangelands monitoring lags behind that in the cropping arena, creating specific challenges in the development of this global system.

System Components and Outputs

It is envisaged that the RAPP will be built from efficient integration of earth observation data, in situ observation data and robust pasture and livestock productivity models. This system will also need to be integrated into national environmental and agricultural statistics.

The RAPP will produce information in a spatially-explicit manner on:

  • the amount of aboveground biomass and the various components of change – for example, the differentiation between expected seasonal variability versus the effects of degradation processes on long run decline versus the impact of drought or flood. To do this, the system will need to quantify the relative proportions of trees, live grass and forbs, dead plant remnants, bare soil – and water, as well as biomass dynamics.
  • the proportion of biomass which is grazed or removed in other ways (eg. hay removal). This will require connection with in situ measurements/monitoring sites, the use of grazing system models (eg. the GRASP model) and the various survey methods for herd estimation and hay removal.

Determining the spatial and temporal extent

To maximise its utility as a spatially explicit rangeland-management-support system, the RAPP system will use wall-to-wall satellite data and standardised land-cover mapping approaches, integrated with ground measurements of aboveground biomass and simulation modelling. The scale and frequency will be routinely revisited and refined through end-user and expert consultation – suggested components are likely to include:

  • finest scale, lowest frequency: 25m; yearly
  • broadest scale, highest frequency: 250-500m, seasonal
  • selected global datasets that more directly relate to standing biomass (e.g. spaceborne stereo imaging, spaceborne lidar and SAR)
  • integrated simulation modelling – process-based and/or probabilistic predictions

Input data sources

Regardless of the final scale and frequency chosen, input-data sources are likely to include:

  • time series remote sensing of biomass and change (optical multispectral and SAR)
  • in situ measurement of aboveground biomass using techniques ranging from conventional destructive sampling to proximal sensing devices (e.g. terrestrial lidar)
  • ancillary data:
    • land use and tenure maps
    • pasture and woodland productivity models
    • survey data of various kinds – crucial to connect to data on animal distribution and movement
    • climatological, soils and soil-moisture data
    • the connection from local-to-global consumption and animal protein production

In addition, there are a number of measurements, modelling and monitoring activities, which are complementary to the aims of the sub-task, and are an essential connection. These include:

  • land condition activities where the focus is on desertification prevention, biodiversity protection and related aims; and
  • soil degradation monitoring developments, which sense ground cover dynamics, proportion of bare uncovered soil and algorithms to fractionate the cover into green and senescent vegetation and various forms of soil exposure (the accent in these programs has been on the modelling and prevention of soil erosion by wind or water).

While these programs are secondary to the core focus on biomass and food, they are important components to the overall environmental sustainability goals of the sub-task.