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by Jan Cees Venema

Data portal aims to help unlock food production bottlenecks

FAO and IIASA launch online Global Agro-ecological Zones Interactive Data Portal

25 May 2012, Rome – A new online data portal developed by the Food and Agriculture Organization of the United Nations (FAO) and the International Institute for Applied Systems Analysis (IIASA) aims to help unlock the planet’s potential to feed a rapidly growing population.

The Global Agro-ecological Zones (GAEZ) Portal developed by FAO and IIASA is a planning tool designed to help to identify areas for   increased global food production while maintaining natural resources base and facing the challenge of climate change. According to FAO estimates, world food production needs to increase 60 percent by 2050 to feed a world population expected to surpass 9 billion people.

Much of the necessary growth will need to be achieved by increasing the amount of food produced on existing agricultural land, as most of the world’s best farmland is already being used.

Water scarcity is another limiting factor for area expansion. And intensification of food production will occur within a changing climate, requiring adaptation and mitigation and will have to be sustainable to safeguard future use of the resources.

A critical first step in sustainably intensifying food production is to close the “yield gaps” that continue to plague the farming sector in many parts of the world.

“GAEZ can help identify where there are ‘bridgeable yield gaps’ and what causes them, allowing for the formulation of appropriate investment policies and the provision of appropriate support to farmers to help them produce more food” says Parviz Koohafkan, Director of FAO’s Land and Water Division.

The term “yield gap” refers to the difference between how much food a farm actually produces and how much food it would be capable of producing if appropriate practices, inputs, technologies and knowledge were applied.

Such gaps can be quite wide: for example, a recent FAO study found that in some rural areas of Eastern Europe, the Caucasus and Central Asia, crop production by small farmers, especially for cereals, can run as low as low as just 30-40 percent of potential.

The world region with the highest yield gaps is sub-Saharan Africa. Cereal yields in Africa as a whole have long hovered around 1.2 tons per hectare, compared to an average yield of some 3 tons per hectare in the developing world as a whole.

A wellspring of data, online

A new online data portal developed by FAO and the International Institute for Applied Systems Analysis (IIASA) seeks to enhance planners’ and decision makers’ capacity to estimate agricultural production potentials and variability under different environmental and management scenarios, including climatic conditions, management regimes, water availability and levels of inputs.

The portal — the Global Agro-Ecological Zones Interactive Data Access Facilities — offers access to what IIASA Director/CEO Pavel Kabat calls “the most ambitious global agro-resources assessment ever conducted”. “The objective was to assemble a vast wealth of data information and make this available in a way that is most accessible to land use planners and specialists to help close yield gaps and promote the sustainable intensification of agricultural production,” Kabat says.

At the heart of the GAEZ system is an extensive inventory of the world’s agricultural resources and related data, organized around five thematic areas:

  • Land and water resources, including multiple spatial layers of climate, soil, terrain, land cover, irrigation potentials, protected areas, population density, livestock density and accessibility, etc.
  • Agro-climatic resources,providing major climatic indicators important for assessing crop growth, development and yield formation. GAEZ’s spatial agro-climatic inventories of the prevailing thermal and moisture regimes and growing periods are used for estimating crop suitability and potential yields.
  • Agricultural suitability and potential yields, including information on yield constraints, crop calendars, and production potential estimates for 11 major crop groups, 49 major crops and 92 crop types. Productivity estimates are made for rain-fed farming, rain-fed farming with water conservation and gravity, sprinkler and drip irrigation systems.
  • Actual yields and production, consisting of spatially explicit crop production estimates including crop harvested area, yield and production figures for 23 major commodities.
  • Yield and production gaps, which provide important information on locations with differences between actual achieved and potential attainable yield and production under different management scenarios.

Being geo-referenced, GAEZ allows a user to identify agricultural zones across the globe that share similar ecological conditions and are producing the same crops using the same kinds of production system, but which do not have the same production levels. This means the reasons underlying lower production – inadequate or inappropriate agricultural practices, policies, institutions, support services and access to markets. – can be pinpointed and dealt with. The potential exists to expand food production efficiently while limiting impacts on other ecosystem values.

In particular, given the scarcity of suitable resources in some regions, future demand and expected negative impacts of climate change, GAEZ would allow users to evaluate options for more widespread adoption of sustainable land and water management practices in agricultural systems at risk, recently highlighted in FAO’s report The State of the World’s Land and Water Resources for Food and Agriculture.

These systems at risk face the threat of progressive breakdown of their productive capacity. They warrant priority attention for remedial action simply because there are no substitutes.

Alexander Mueller, Assistant Director General of the FAO Natural Resources Management and Environment Department, which developed GAEZ in collaboration with IIASA, concludes: “the new GAEZ data portal will provide a global tool to manage natural resources for food and agriculture in a more sustainable way. Natural resources are the basis for food production. In a world already facing today water scarcity and land degradation in many areas and coping with increasing risks from climate change, this is the only way to achieve food security.”

[from: FAO Media Center]

by Jan Cees Venema

Integrating crop growth simulation and remote sensing to improve resource use efficiency in farming systems

Wageningen University dissertation no. 3951

This study investigated the scope and constraints for integrated use of mechanistic crop growth simulation models and earth observation techniques. Integration of high-quality crop growth models and information derived from earth observations can contribute to improved use of resources, reduced crop production risks, reduced environmental degradation, and increased farm income. In the past, both, simulation modelling and remote sensing have been shown to be valuable tools in separate applications in agriculture. Crop growth simulation has made valuable contributions to yield forecasting, proto-typing crop varieties, generation of input-output coefficients for improved agricultural production technologies and to management decision support systems at field level. Likewise, remote sensing techniques have been successfully applied in classification of arable crops and in quantification of vegetation characteristics at different spatial and temporal scales. The starting point of this study was the hypothesis that integration of both techniques would lead to improvements in the dynamic simulation of the crop-soil system and thus contribute to improvements in management decision support systems for environmentally sound agricultural production.

Thus far, mutually beneficial linkages have been limited to land use classification via remote sensing (choice of adequate model) and quantification of crop growth and development curves using e.g. estimates of leaf area indices derived from remote sensing images for model calibration under (usually) favourable growth conditions. Only a few studies have considered the potentials of remote sensing for model initialization of growth and development characteristics of a specific crop. In this thesis these potentials have been extended to a more continuous approach, in which remote sensing information is not only used in model initialization, but also in model calibration in the course of the simulation run, so-called run-time calibration. During such a run-time calibration procedure, simulated values of e.g. leaf area index (LAI) and canopy nitrogen status (CNS) are replaced by values estimated from remote sensing images acquired at different stages in the course of the growing period. LAI and CNS are important controlling variables in models for arable crops such as wheat, potato and maize. This run-time calibration procedure has been performed for a full crop growth cycle, for optimal as well as sub-optimal growth conditions. This approach enables spatial differentiation in crop growth simulation, as variations in crop status, resulting from differences in growth conditions, lead to differences in remote sensing signals. The relationships between near and remote sensing observations at leaf, plant and canopy level have been investigated and the effects of variations in estimated values of LAI and CNS used in run-time calibration of dynamic crop growth simulation models on final model results (e.g. crop yield) have been analyzed.

Results from potato trials in the Netherlands show that leaf nitrogen contents derived from near sensing observations can be up-scaled to plant and canopy nitrogen status by taking into account the vertical nitrogen distribution in the crop. A vertical nitrogen extinction coefficient (kn) of 0.41 resulted in an accuracy increase of the relation between leaf nitrogen (g N m-2 leaf) and SPAD readings (a near sensing technique at leaf level), with a correlation coefficient (r2) of 0.91. Remote sensing observations integrate nitrogen contents over canopy depth and do not require adjustment for vertical nitrogen gradients, if canopy nitrogen status is expressed in total nitrogen content per unit of soil surface. The red edge position (an index derived from remote sensing observations) could be related to canopy nitrogen content (g N m-2 soil) with a correlation coefficient (r2) of 0.82. Leaf area indices of potato (Netherlands) and maize (Argentina, France, USA) crops, for use in run-time calibration, were also accurately derived from field, airborne and spaceborne remote sensing platforms. Introducing LAI values derived from RS in the simulation model and concurrently adjusting CNS by retaining leaf N-concentrations, led to more accurate simulation results for CNS than without such adjustment. The different crops, and the range in environmental conditions, soil fertility status and management practices that were examined in the different case-studies in this thesis, have demonstrated the broad applicability of mechanistic simulation models integrated with remote sensing information

Winter wheat fields, wheat phenological stages (emergence, flowering) and management operations (harvest) were successfully identified on the basis of information from optical and radar remote sensing data in a case-study in South-eastern France. Timing of these phenological stages and management operations is important in model calibration as they mark the length of the crop growth period and of the grain-filling period, which are co-determinants of grain yield. At flowering, C-band radar backscatter from the soil is maximally reduced by canopy moisture content. This characteristic was successfully used to estimate regional wheat flowering dates. Integration of RS data in the (point-based) crop growth simulation model allowed its spatial application for prediction of wheat production at regional scale. The estimated value was in agreement with regional yield statistics. This integration thus allows expansion of the application area of valuable research tools, as up-scaling has become feasible.

Introduction of remote sensing-based estimates of LAI and CNS in the course of the growing seasons into dynamic simulation of the growth of potato and maize resulted in improved simulation accuracy for aerial crop characteristics, as well as for variables that could not be directly observed by remote sensing, such as soil inorganic nitrogen contents. The degree of success and robustness of the integrated approach depends on the timing, accuracy and number of remote sensing observations available for re-setting the relevant state variables in the course of the simulation period. Simulation accuracy was positively correlated with the number of observation dates from remote sensing. Remote sensing observations around flowering had more impact on calculated final grain yield (FGY) for maize than earlier or later observations.

The investigations reported in this thesis have shown that the accuracy of predictions of dynamic and mechanistic crop growth simulation models significantly improves through integrating earth observation-derived information as input for the models and for their run-time calibration. Such integration not only yields more accurate estimates of crop bio-physical variables, such as leaf area index and canopy nitrogen status, but also contributes to improved prediction at regional scales. Such models, producing reliable, site-specific predictions of crop performance and crop requirements are thus effective tools in the development of environmentally-friendly production methods and in optimizing the use of our natural resources.

Further research should focus on the scope for estimating additional crop variables of interest for integration in simulation modelling through remote sensing. Management interventions may be triggered by various crop characteristics, such as: 1) canopy temperatures derived from thermal remote sensing systems as an indicator for water stress, 2) canopy discolouring derived from optical remote sensing systems as an indicator for nutrient shortages and 3) canopy architecture derived from radar remote sensing images as an indicator for water and nutrient supply. Remote sensing is also a valuable technique to identify spatial patterns of crop performance and crop status within arable fields. Moreover, remote sensing allows identification of patterns that may be related to specific diseases or special events, such as outbreaks of phytophtera in potato, or lodging in grain crops.

This study has demonstrated that a decision support system for crop and soil management based on the integration of crop growth simulation modelling and remotely sensed data is within reach. In addition, nitrogen uptake, its vertical distribution within the crop, and the inorganic nitrogen content of the soil can be simulated more accurately with such an integrated system. Such a decision support system can be used for fine-tuning of fertilizer regimes thus contributing to more environmentally sound and sustained agricultural production.

ref. Plant Production Systems Group – Wageningen UR – Wageningen University.

by Jan Cees Venema

On Global Agro-Ecological Zones

Land is an indispensable resource for the most essential human activities: it provides the basis for agriculture and forest production, water catchment, recreation, and settlement. The range of uses that can be made of land for human needs, is limited by environmental factors including climate, topography and soil characteristics, and is to a large extent determined by demographic, socio-economic, cultural, and political factors, such as population density, land tenure, markets, institutions, and agricultural policies.

In most developing countries, the needs and demands of rapidly increasing populations have been the principal driving force in the allocation of land resources to various kinds of uses, with food production as the primary land use. Population pressure and an increased competition among different land users have emphasized the need for more effective land-use planning and policies. Rational and sustainable land use is an issue of great concern to governments and to land users interested in preserving the land resources for the benefit of present and future populations. An integrated approach to planning and management of land resources is a key factor to implementing solutions which will ensure that land is allocated to uses providing the greatest sustainable benefit.

The increasing human population in several developing countries is placing pressure on the finite land resources, risking over-exploitation and land degradation. Sectoral and single objective approaches used to alleviate this situation have frequently not been effective. An integrated approach is required that involves all stakeholders, accommodates the qualities and limitations of each land unit, and produces viable land use options (FAO, 1995a).

 

Agro-Ecological Zones Approach

The Food and Agriculture Organization of the United Nations (FAO) with the collaboration of the International Institute for Applied Systems Analysis (IIASA), has developed a system, that enables rational land use planning on the basis of an inventory of land resources and evaluation of biophysical limitations and potentials. This is referred to as the Agro-ecological Zones (AEZ) methodology.

The AEZ methodology utilizes a land resources inventory to assess, for specified management conditions and levels of inputs, all feasible agricultural land-use options and to quantify expected production of cropping activities relevant in the specific agro-ecological context. The characterization of land resources includes components of climate, soils and landform, which are basic for the supply of water, energy, nutrients and physical support to plants.

Recent availability of digital global databases of climatic parameters, topography, soil and terrain, and land cover has allowed for revisions and improvements in calculation procedures and to expand assessments of AEZ crop suitability and land productivity potentials to temperate and boreal environments. This effectively enables global coverage for assessments of agricultural potentials and has led to this Global AEZ study.

via GAEZ Global Agro-Ecological Zones.

by Jan Cees Venema

World Water Day 2012 – Water and Food Security – 22nd March 2012

Why is water a key to food security?

Food security exists when all people at all times have both physical and economic access to sufficient, safe and nutritious food that meets their dietary needs for an active and healthy life.

People who have better access to water tend to have lower levels of undernourishment. The lack of water can be a major cause of famine and undernourishment, in particular in areas where people depend on local agriculture for food and income.

Erratic rainfall and seasonal differences in water availability can cause temporary food shortages. Floods and droughts can cause some of the most intensive food emergencies.

Read more>>  World Water Day 2012 – Water and Food Security – 22nd March 2012.