Crop Modeling and Remote Sensing is Need of the Time

gm-cropsCrop Modeling

A model is a schematic representation of the conception of a system or an act of mimicry or a set of equations, which represents the behavior of a system. Also, a model is “A representation of an object, system or idea in some for mother than that of the entity itself ”. Its purpose is usually to aid in explaining, understanding or improving performance of a system.

A model is, by definition“A simplified version of a part of reality, not a one to one copy”. This simplification makes models useful because it offers a comprehensive description of a problem situation. However, the simplification is, at the same time, the greatest drawback of the process. It is a difficult task to produce a comprehensible, operational representation of a part of reality, which grasps  the  essential  elements  and  mechanisms  of  that  real  world  system  and  even  more demanding, when the complex systems encountered in environmental management. The Earth’s land resources are finite, whereas the number of people that the land must support continues to grow rapidly. This creates a major problem for agriculture. The production (productivity) must be

increased to meet rapidly growing demands while natural resources must be protected. New agricultural research is needed to supply information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate around the world. In this direction explanation and prediction of growth of managed and natural ecosystems in response to climate and soil-related factors are increasingly important as objectives of science. Quantitative prediction of complex systems, however, depends on integrating information through levels of organization, and the principal approach for that is through the construction of statistical and simulation models. Simulation of system’s use and balance of carbon, beginning with the input of carbon from canopy assimilation forms the essential core of most simulations that deal with the growth of vegetation. Crop modeling is a tool which help us inn all this scenario within short time.

Remote Sensing

Remote  sensing  techniques  are widely used  in agriculture and agronomy.  The  use of  remote sensing is necessary, as the monitoring of agricultural activities faces special problems not common to other economic sectors. First of  all, agricultural  production follows strong seasonal patterns related to the biological lifecycle of crops. The production depends secondly on the physical landscape (e.g., soil type), as well as climatic driving variables and agricultural management practices. All variables are highly variable in space and time. Moreover, as productivity can change within short time periods, due to unfavorable growing conditions, agricultural monitoring systems need to be timely. This is even more important, as many items are perishable. Thus, as pointed out by the Food and Agriculture Organization (FAO) (2011), the need for timeliness is a major factor underlying agricultural statistics and associated monitoring systems—information is worth little if it becomes available too late. Remote sensing can significantly contribute to providing a timely and accurate picture of the agricultural sector, as it is very suitable for gathering information over large areas with high revisit frequency. It provides arguments for enhancing investments in agricultural monitoring systems. It follows the strong conviction that a close monitoring of agricultural production systems is necessary, as agriculture must strongly increase its production for feeding the nine-billion people predicted by mid-century. This increase in production must be achieved while minimizing the environmental impact of agriculture. Achieving this goal is difficult, as agriculture must cope with climate  change  and  compete  with  land  users  not  involved  in  food  production  (e.g.,  biofuel

production, urban expansion, etc.). The necessary changes and transitions have to be monitored closely to provide decision makers with feedback on their policies and investments.

On  the  one  hand,  the  identification  of  drought  and  the  potential  shortages  likely  to  occur  in developing countries as a result are central to government and international response programmes and relief efforts; on the other hand burgeoning subsidies in industrial agriculture have necessitated the development of sophisticated techniques to maximise the effectiveness of these subsidies in controlling production. A third, related component of crop assessment has recently begun to emerge in the form of individual crop forecasting, for example for sugar beet and potato, allowing a complex market for such crops to develop.

While it is farmers that strive for profitable, efficient and sustainable production from renewable resources (crops in particular but also livestock, timber and forage), it is increasingly the decision makers and planners who have to address and respond to issues of over- and/or under-production, imports, exports and quotas, conservation and protection, food security, subsidy allocation and administration. Explicit within this mandate is federal production levels, in particular crop assessment including areas under production, yields, predictions/forecasts, changing land use and land ownership, changing management and technical inputs, farming systems and actual crops planted and harvested.

When adequate information on these component parts of the agricultural system are available or can be collected, political and economic concerns can be addressed through improved management programmes to ensure both the sustainable utilization of the available resources for food and of appropriate  high  level  decisions  regarding  food  movements,  pricing  and  imports/exports.  The premier way of acquiring this data in a cost-effective and synoptic way is through the use of rigorous remote sensing methodologies.

The use of remote sensing for data gathering, allied to the introduction of Geographic Information Systems (GIS) as a powerful tool to process that data in conjunction with information collected using traditional field techniques helps overcome traditional data volume constraints.

Climate Scenario

Climate change and agriculture are interrelated processes, both of which take place on a global scale. Climate change affects agriculture in a number of ways, including through changes in average temperatures, rainfall, and climate extremes (e.g., heat waves); changes in pests and diseases; changes in atmospheric carbon dioxide and ground-level ozone concentrations; changes in the nutritional quality of some foods; and changes in sea level.

Climate change is already affecting agriculture, with effects unevenly distributed across the world. Future climate change will likely negatively affect crop production in low latitude countries, while effects in northern latitudes may be positive or negative. Climate change will probably increase the risk of food insecurity for some vulnerable groups, such as the poor.

Agriculture contributes to climate change by anthropogenic emissions of greenhouse gases (GHGs), and by the  conversion of  non-agricultural land (e.g.,  forests)  into agricultural land.  Agriculture, forestry and land-use change contributed around 20 to 25% to global annual emissions. Average global temperatures are expected to increase by 2°F to 11.5°F by 2100, depending on the level of future greenhouse gas emissions, and the outcomes from various climate models. By 2100, global average temperature is expected to warm at least twice as much as it has during the last 100 years. Ground-level air temperatures are expected to continue to warm more rapidly over land than oceans. Some parts of the world are projected to see larger temperature increases than the global average.



Saif Ali1, Muhammad Umair Hassan1, Muhammad Mahran Aslam2, Rashid Hussain3  and Ramla


Department of 1Agronomy, 2Plant Breeding and Genetics and 3Institute of Horticultural Sciences, University of Agriculture Faisalabad


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