Global Aerial Imaging Market Will Thrive Following the Rising Popularity in Precision Agriculture Through 2020

The research study covers the present scenario and growth prospects of the global aerial imaging market for 2016-2020. To calculate the market size, the report considers the following:

  • The retail price of aerial imagery
  • The revenue generated from aerial imagery in business sectors, including oil and gas, mining, agriculture and forestry, building and infrastructure, government, sustainable energy, and military and defense
  • The revenue generated from leasing equipment (drones) for aerial imagery and aerial surveying in commercial applications

Technavio ICT analysts highlight the following four factors that are contributing to the growth of the global aerial imaging market:

  • High adoption in urban planning
  • Rising popularity in precision agriculture
  • Remote sensing and GIS in disaster management
  • Low-cost sensor drone technology

High adoption in urban planning

High-resolution digital aerial imagery has gained popularity among planners, developers, and engineers for real estate management, land calculations, road planning, and small-scale mapping for most land applications.

Information from aerial images, along with GIS mapping, is used for analysis, strategic planning, and evaluation in engineering and urban planning. In addition, aerial images support professional response and recovery agencies, governments, and communities to recover from hazards, including natural disasters.

According to Rakesh Kumar Panda, a lead analyst at Technavio for M2M and connected devices, “Aerial imagery provides a great deal of information on a project site, which is not visible on the ground level. These aerial images provide a thorough approach for site identification, evaluation, and selection. Aerial surveys assess land suitability and capability for future use. The images offer an elevated perspective, helping urban developers identify land use opportunities, the feasibility of proposals, and required design changes.”

Rising popularity in precision agriculture

Geospatial technologies are used in precision agriculture to map spatial variations in crop and soil conditions. They match inputs related to water, seed, and fertilizers to the variations by applying them at variable rates.

Aerial photography gathers the necessary information for crop analysis and management. The normalized difference vegetation index (NDVI) can be measured using aerial imagery, which indicates the green vegetation levels of crops. Aerial imagery can be used to monitor crops, forests, and ecosystems for subtle changes in visible and near-infrared radiations.

“Due to technological advances in aerial imagery, farmers and ranchers worldwide are showing increased interest in aerial photography services. Aerial thermal photography helps determine crop temperatures, and along with NDVI, it provides comprehensive information on plants that need to be irrigated,” says Rakesh.

Remote sensing and GIS in disaster management

Natural disasters such as earthquakes, landslides, floods, fires, and cyclones cause huge property and infrastructure loss. Remotely sensed aerial data can be used to assess the severity and impact of the damage posed by these disasters. GIS can be used to manage large amounts of data required for vulnerability and hazard assessment during the catastrophe prevention stage. Also, it acts as a tool for planning evacuation routes, designing centers for emergency operations, and integrating aerial imagery data with other relevant information for the design of disaster warning systems during the catastrophe preparedness stage.

Low-cost sensor drone technology

Innovations in drone technology have facilitated the transition from high-cost fixed wing aircraft to cheaper and more efficient UAV LiDAR models. Low-cost UAV LiDAR systems find applications commercial, government, and environment and conservation sectors.

Inexpensive drones with advanced sensors and imaging capabilities enable precision agriculture, which helps farmers reduce crop damage and increase crop yield. Low cost of technology, along with the easy integration of multiple technologies, provides surveyors and consulting engineers a significant opportunity for development. The cost of a high-end GPS-controlled drone with a camera is around USD 6,000. Military agencies can replace older warships and aircraft with low-cost drones to handle the intelligence aspects. This replacement will help reduce costs related to upgrading obsolete war equipment.

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Image masking for crop yield forecasting using AVHRR NDVI time series imagery

Time series of the Advanced Very High-Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) have been used for crop yield forecasting since the 1980s. Image masking was a critical component of several of these yield forecasting efforts as researchers attempted to isolate subsets of a region’s pixels that would improve their modeling results. Approaches generally sought to identify cropped pixels and when possible, pixels that corresponded to the particular crop type under investigation. In this paper, the former approach will be referred to as cropland masking, and the latter approach will be called crop-specific masking.
The research presented in this paper examines the underlying assumptions made when image masking for the purpose of regional crop yield forecasting. An alternative statistical image masking approach (called yield-correlation masking) is proposed that is objective (i.e., can be automated) and has the flexibility to be applied to any region with several years of time series imagery and corresponding historical crop yield information. The primary appeal of yield-correlation masking is that, unlike cropland masking, no land cover map is required, yet we will show that NDVI models generated using the two methods demonstrate comparable predictive ability.

The goal of this research is very specific: We will establish the yield-correlation masking procedure as a viable image masking technique in the context of crop yield forecasting. To accomplish this objective, we empirically evaluate and compare cropland masking and yield-correlation masking for the purpose of crop yield forecasting. Cropland masking has been shown to benefit yield forecasting models, thus providing a practical benchmark. In the process, we present a robust statistical yield forecasting protocol that can be applied to any (region, crop)-pair possessing the requisite data, and this protocol is used to evaluate the two masking methods that are being compared.
This paper is developed as follows. A brief review of related research is presented. The two primary data sets, AVHRR NDVI time-series imagery and United States Department of Agriculture (USDA) regional crop yield data, are described. Details of the study regions, crop types, and time periods under investigation are specified. Three image masking procedures are discussed, two of which are evaluated in the research.
Finally, the modeling approach and performance evaluation framework are described, along with a summary of results and conclusions drawn from the analysis. Details of the modeling strategy used are described in the Appendix.
Related research
Traditionally, yield estimations are made through agrometeorological modeling or by compiling survey information provided throughout the growing season. Yield estimates derived from agro-meteorological models use soil properties and daily weather data as inputs to simulate various plant processes at a field level (Wiegand & Richardson, 1990; Wiegand et al., 1986). At this scale, agro-meteorological crop yield modeling provides useful results. However, at regional
scales these models are of limited practical use because of spatial differences in soil characteristics and crop growthdetermining factors such as nutrition levels, plant disease, herbicide and insecticide use, crop type, and crop variety, which would make informational and analytical costs excessive. Additionally, Rudorff and Batista (1991) indicated that, at a regional level, agro-meteorological models are unable to completely simulate the different crop growing conditions that result from differences in climate, local weather conditions, and land management practices. The scale of applicability of agrometeorological models is getting larger, though, but presently only through the integration of remotely sensed imagery. For instance, Doraiswamy et al. (2003) developed a method using AVHRR NDVI data as proxy inputs to an agro-meteorological model in estimating spring wheat yields at county and subcounty scales in the U.S. state of North Dakota.
In the past 25 years, many scientists have utilized remote sensing techniques to assess agricultural yield, production, and crop condition. Wiegand et al. (1979) and Tucker et al. (1980) first identified a relationship between the NDVI and crop yield using experimental fields and ground-based spectral radiometer measurements. Final grain yields were found to be highly correlated with accumulated NDVI (a summation of NDVI between two dates) around the time of maximum greenness
(Tucker et al., 1980). In another experimental study, Das et al. (1993) used remotely sensed data to predict wheat yield 85– 110 days before harvest in India. These early experiments identified relationships between NDVI and crop response, paving the way for crop yield estimation using satellite imagery.
Rasmussen (1992) used 34 AVHRR images of Burkina Faso, Africa, for a single growing season to estimate millet yield. Using accumulated NDVI and statistical regression techniques, he found strong correlations between accumulated NDVI and yield, but only during the reproductive stages of crop growth. The lack of a strong correlation between accumulated NDVI and yield during other stages of growth was attributed to the limited temporal profile of imagery used in the study and the high variability of millet yield in his study area. Potdar (1993) estimated sorghum yield in India using 14 AVHRR images from the same growing season. He was able to forecast actual yield at an accuracy of T15% up to 45 days before harvest. Rudorff and Batista (1991) used NDVI values
as inputs into an agro-meteorological model to explain nearly 70% of the variation in 1986 wheat yields in Brazil. Hayes and Decker (1996) used AVHRR NDVI data to explain more than 50% of the variation in corn yields in the United States Corn Belt. Each of these studies found positive relationships between crop yield and NDVI, but the strength of the relationships depended upon the amount and quality of the imagery used.
Some studies have used large, multi-year AVHRR NDVI data sets. Maselli et al. (1992) found strong correlations between NDVI and final crop yields in the Sahel region of Niger using 3 years of AVHRR imagery (60 images). In India, Gupta et al. (1993) used 3 years of AVHRR data to estimate wheat yields within T5% up to 75 days before harvest. The success of this study was dependent on the fact that over 80% of the study area was covered with wheat. In Greece, 2 years of AVHRR imagery were used to estimate crop yields (Quarmby et al., 1993). Actual harvested rice yields were predicted with an accuracy of T10%, and wheat yields were predicted with an accuracy of T12% at the time of maximum greenness. Groten (1993) was able to predict crop yield with a T15% estimation error 60 days before harvest in Burkina Faso using regression techniques and 5 years of AVHRR NDVI data (41 images).
Doraiswamy and Cook (1995) used 3 years of AVHRR NDVI imagery to assess spring wheat yields in North and South Dakota in the United States. They concluded that the most promising way to improve the use of AVHRR NDVI for estimating crop yields at regional scales would be to use larger temporal data sets, better crop masks, and climate data. Lee et al. (1999) used a 10-year, biweekly AVHRR data set to forecast  corn yields in the U.S. state of Iowa. They found that the most accurate forecasts of crop yield were made using a cropland mask and measurements of accumulated NDVI. Maselli and Rembold (2001) used multi-year series of annual crop yields and monthly NDVI to develop cropland masks for four Mediterranean African countries. They found that application of the derived cropland masks improved relationships between NDVI and final yield during optimal yield prediction periods. Ferencz et al. (2004) found yields of eight different crops in Hungary to be highly correlated with optimized, weighted seasonal NDVI sums using 1-km AVHRR NDVI from 1996 to 2000. They used non-forest vegetation masks and a novel time series interpolation approach and actually obtained their best results when using a greenness index equivalent to the numerator of the NDVI formula (NIR-RED; see Section 3). Additionally, many researchers have found that crop condition and yield estimation are improved through the inclusion of metrics that characterize crop development stage (Badhwar & Henderson, 1981; Groten, 1993; Kastens, 1998; Lee et al., 1999; Quarmby et al., 1993; Rasmussen, 1992). Ancillary data have been found useful as well. For example, Rasmussen (1997) found that soil type information improved the explanation of millet and ground nut yield variation using 3 years of AVHRR NDVI from the Peanut Basin in Senegal. In a later study, Rasmussen (1998) found that the inclusion of tropical livestock unit density further improved the explanation of millet yield variation in intensively cultivated regions of the Peanut Basin.

Based on the studies described, for the purpose of crop yield forecasting, longer time series of NDVI imagery are preferred to shorter ones. Also, few image masking techniques have been thoroughly and comparatively explored, likely due to the inherent complexities underlying this phase in any remote sensing-based yield forecasting methodology. Thus, to help achieve the goal of this project, an important objective of this research is to use historical yield information and historical time series AVHRR NDVI imagery to devise a thorough and robust statistical procedure for obtaining early to mid-season crop yield forecasts, with particular emphasis on image masking. The techniques described in this paper can be applied to any (region, crop)-pair that possesses sufficient historical yield information and corresponding time series NDVI imagery. Since few meaningful crop phenology metrics can be accurately derived at early points in the growing season, our research does not attempt to use this information. Also, no ancillary information is used, to prevent dependence on the availability of such data.

Description of data
The research presented in this paper relies on two data sets. The first is a time series of biweekly AVHRR NDVI compositeimagery from 1989 to 2000, obtained from the U.S. Geological survey Earth Resources Observation Systems (EROS) Data Center (EDC) in Sioux Falls, SD (Eidenshink, 1992). This data set was chosen because it is relatively inexpensive, reliable, and is updated in near real-time. NDVI is defined by the formula (NIR-RED)/(NIR +RED), where NIR is reflectance in the near-infrared spectrum (0.75 – 1.10 Am) and RED is reflectance in the red band of the visible spectrum (0.58 – 0.68 Am). Chlorophyll uses electromagnetic energy in the RED band for photosynthesis, and plant structure is reflective of energy in the NIR band. So, for vegetated surfaces, NDVI increases if plant biomass increases or if photosynthetic activity increases.
The NDVI data were received in unsigned 8-bit integer format, with the original NDVI range [1,1] linearly scaled to the integer range 0 –200. For analysis purposes, the integer data were rescaled to their native range of [1,1]. As a consequence of the limited precision of the original 8-bit data, the precision of the rescaled data is 0.01, so there is an implicit expected numerical error of 0.005 in the pixel-level NDVI values.
The NDVI data set is not without uncertainty, both temporal and spatial. From 1989 – 2000, two polar orbiting National Oceanic and Atmospheric Administration (NOAA) satellites (NOAA-11 [1989 – 1994] and NOAA-14 [1995 – 2000]) carried the AVHRR sensors that collected the data comprising our data set. The U.S. annually invariant target curve, with NDVI from periods 5– 21 (February 26 –October 21) shown for each year. This curve represents the average time series of nearly 3500 pixels selectively sampled from the 48 states in the conterminous U.S. to possess highly regular annual periodicity, thus exposing any artificial interannual NDVI value drift (Kastens et al., 2003). The NDVI data originating from NOAA-11 are fairly consistent over time. The data from NOAA-14 are less so, exhibiting a large artificial oscillation from 1997 to 2000. The range of the trend curve from 1989 to 2000 has width 0.0464. Comparing this width to
the overall effective range of the AVHRR NDVI data being used (which is approximately [0.05,0.95] for the full U.S. terrestrial range, but narrower in most practical situations), it follows that nearly 5% of the effective AVHRR NDVI data range can be attributed to artificial interannual NDVI value drift. In retrospect, we know that sensor orbit decay and sensor calibration degradation were the primary sources of the interannual NDVI value drift found in the NOAA-14 data.
Image resolution (1 km2 /pixel, or 100 ha/pixel) of the AVHRR NDVI data is also an issue because pixel size is more  than twice as large as the typical field size for soybeans and major grains in the U.S., which is roughly 40 ha (Kastens & Dhuyvetter, 2002). Furthermore, when considering spatial error of the image registration performed during the NDVI compositing process, the area of the region from which a single pixel’s values can be obtained grows to more than 4 km2, or more than 400 ha (Eidenshink, 1992). A combination of sensor factors (e.g., sensor stability, view angle, orbit integrity) and effects of image pre-processing and compositing induce this spatial variation.

The second data set is historical, final, state-level yield data, obtained from the USDA National Agricultural Statistics Service (NASS) through its publicly accessible website ( The database is updated annually for all crops, with each particular crop’s final regional yield estimates released well after harvest completion. Updates to the final regional yield estimates can occur up to 3 years after their initial release, but generally these changes are not large. No historical or expected error statistics for these estimates are published below the national spatial scale, but they are nonetheless accepted by the industry as the best widely available record for average regional crop yield in the U.S.

Description of crops, regions, and time periods under investigation
The crops and regions under investigation in the present research are corn and soybeans in the U.S. states of Iowa (IA) and Illinois (IL), winter wheat and grain sorghum in the state of Kansas (KS), and spring wheat and barley in the state of North Dakota (ND). Compared to other states, during 1989 –2000, Iowa ranked first in corn production (100.2 million mt/year; ‘‘mt’’=metric ton) and second in soybean production (26.9 million mt/year). Illinois ranked second in corn production (89.9 million mt/year) and first in soybean production (27.1 million mt/year). Kansas was the top-producing winter wheat state (25.5 million mt/year) and the top-producing grain sorghum state (14.2 million mt/year). North Dakota was the top-producer of both spring wheat (16.6 million mt/year) and barley (6.3 million mt/year).

For each crop, a six-period window of early to mid-season NDVI imagery is considered. The source data for these six biweekly composites span nearly 3 months of raw AVHRR imagery, corresponding to Julian biweekly periods 5 – 10 (approximately February 26 –May 20) for winter wheat, 9– 14 (approximately April 23 – July 15) for spring wheat and barley, and 11 –16 (approximately May 21–August 12) for corn, soybeans, and sorghum. Labeling the six biweekly periods 1 to 6, yields are modeled using data from periods 1– 4, 1 –5, and 1 – 6, with each of these three ranges providing a unique yield forecasting opportunity corresponding to a
different point in the growing season. To obtain the dates for the crop-specific ranges, the initial release dates of USDA NASS yield forecasts were considered. The first NDVI image generated after the release of the initial USDA state-level estimates for the season is assigned to period 6, which fixes periods 1– 5 as well. Initial release dates for USDA state level estimates are approximately May 11 for winter wheat, July 11 for spring wheat and barley, and August 11 for corn, soybeans, and grain sorghum. Hereafter, winter wheat will be classified as an early-season crop, spring wheat and barley as mid-season crops, and corn, soybeans, and grain sorghum as late-season crops. With this timing, forecasts generated at periods 4 and 5 for each crop are produced before any state-level USDA yield estimates are released.

Approaches to image masking in crop yield forecasting
The purpose of image masking in the context of crop yield forecasting is to identify subsets of a region’s pixels that lead to NDVI variable values that are optimal indicators of a particular crop’s final yield. A good image mask should capture the essence (i.e., salient features) of the present year’s growing season with respect to how the crop of interest is progressing. This growing season essence is a combination of climatic and terrestrial factors.
Crop-specific masking
In theory, the ideal approach to image masking for the purpose of crop yield forecasting would be to use crop-specific masking. This would allow one to consider only NDVI information pertaining to the crop of interest. However, when such masking is applied to multiple years of imagery, several difficulties are encountered. Principal among these is the widespread practice of crop rotation, which suggests that year-specific masks are needed rather than a single cropspecific mask that can be applied to all years. Regional trending in crop area (increase or decrease in the amount of a region’s area planted to a particular crop over time), if severe
enough, also may call for year-specific masking. Identifying a particular crop in the year to be forecasted presents even greater difficulties, as only incomplete growing season NDVI information is available. This is especially true early in the season when the crop has low biomass and does not produce a large
NDVI response. In addition to hindering crop classification, this low NDVI response of a crop early in its development also  stifles crop yield modeling efforts, as AVHRR NDVI measurements from pixels corresponding to immature crops are not very sensitive and are thus minimally informative (see Wardlow et al., in press, for an example of such insensitivity occurring with 250-m Moderate Resolution Imaging Spectroradiometer [MODIS] NDVI data, and MODIS has better radiometric resolution than AVHRR). Moreover, with the coarse-resolution (about 100 hectares/ pixel) AVHRR NDVI imagery used in this study, identifying monocropped pixels becomes an improbable task. This is particularly true in low-producing regions and in regions with sparse crop distribution. As noted, a single pixel covers an area well over twice the average field size, and when error of the image registration is considered, a pixel’s effective ground coverage can become more than 400 hectares/pixel, or roughly ten times the typical field size.

Cropland masking
A more feasible alternative to crop-specific masking is cropland masking, which refers to using pixels dominated by land in general agricultural crop production. Kastens (1998, 2000) and Lee et al. (1999) obtained some of their best yield modeling results using this approach. Rasmussen (1998) used a percent-cropland map to improve his yield modeling by splitting the data into two categories based on cropland density and building different models for the two classes. Maselli and Rembold (2001) used correlations between 13 years of monthly NDVI composites and 13- year series of national crop yields to estimate pixel-level cropland fraction for four Mediterranean African countries. Upon application of these derived cropland masks, the authors found improved relationships between NDVI and
final estimated yield.
Cropland masks usually are derived from existing land use/land cover maps (one exception being Maselli and Rembold (2001)). If relatively small amounts of land in a study area have been taken out of or put into agricultural crop production during a study period, a single mask can be obtained and applied to all years of data. Considering that all traditional agricultural crops are now lumped in the general class of ‘‘cropland,’’ heavily cropped pixels are more prevalent in heavily cropped regions, which allows for the construction of well-populated masks dominated by cropland. But the generation of such masks becomes difficult when low-producing regions are encountered, as well as in regions where cropland is widely interspersed with non-cropland.
As with crop-specific masking, cropland masking also can suffer the effects of minimally informative NDVI response early in a crop’s growing season (e.g., March for early-season crops, May for mid-season crops, and late May and early June for late-season crops). Many important agricultural regions are almost completely dominated by single-season crop types (e.g., Iowa produces predominately late-season crops). In such cases, cropland AVHRR NDVI from time periods early in the
particular growing season may not be very useful for predicting final yield.

By late May and June, some of the year’s terrestrial and weather-based growth-limiting factors may have already been established for some crops or regions. For instance, in the U.S., soil moisture has largely been set by this time, and soil moisture is an important determinant of crop yields in the four states comprising our study area. Such moisture information is not readily detectable in immature crops because it is usually not a limiting factor until the plant’s water needs become
significant and its roots penetrate deeper into the soil. On the other hand, available soil moisture can noticeably affect other regional vegetation that is already well developed, such as grasslands, shrublands, and wooded areas, and in some cases, early- and mid-season crops.

Yield-correlation masking
For the reasons noted above, we propose a new masking technique, which we call yield-correlation masking. All vegetation in a region integrates the season’s cumulative growing conditions in some fashion and may be more indicative of a crop’s potential than the crop itself. Thus, all pixels are considered for use in crop yield prediction. This premise is most sound early in a crop growing season (especially for mid- and late-season crops), when the NDVI response of the immature crop is not yet strong enough to be a useful indicator of final yield. Also, as noted, when the crop is in early growth stages, problems such as lack of subsoil moisture may not yet have impacted the immature crop while having already affected more mature nearby vegetation.
Each NDVI-based variable captures a different aspect of the current growing season. This aspect manifests itself in different ways within the region’s vegetation, suggesting that optimal masks for the different NDVI-based variables likely will not be identical. Thus, for each (region, crop)-pair, yield-correlation masking generates a unique mask for each NDVI variable. The technique is initiated by correlating each of the historical, pixel-level NDVI variable values with the
region’s final yield history, a strategy similar to the initial step of the cropland classification strategy presented in Maselli and Rembold (2001). The highest correlating pixels,  thresholded so that some pre-specified number of pixels is included in the mask (this issue is addressed later), are retained for further processing and evaluation of the variable at hand.
Though much more computationally intensive, the yieldcorrelation masking technique overcomes the major problems afflicting crop-specific masking and cropland masking. Unlike these approaches, yield-correlation masking readily can be applied to low-producing regions and regions possessing sparse crop distribution. Also, since yield correlation masks are not constrained to include pixels dominated by cropland, they are not necessarily hindered by the weak and insensitive NDVI
responses exhibited by crops early in their respective growing seasons. Furthermore, once the issue of identifying optimal mask size (i.e., determining how many pixels should be  included in the masks) is addressed, the entire masking/ modeling procedure becomes completely objective.

Description of cropland masks
For Iowa, Illinois, and North Dakota, the cropland masks used in this study were derived from the United States Geological Survey (USGS) National Land Cover Database (NLCD) (Vogelmann et al., 2001). The original 30-m resolution land cover maps can be obtained from the website After generalizing the classes to cropland and non-cropland, the data were aggregated to a 1-km grid corresponding to the NDVI imagery used in this study. All annual crops, as well as alfalfa, were assigned to the cropland category, and all other cover types
were classified as non-cropland. Pixel values in the resulting  map corresponded to percent cropland within the 1-km2 footprint of the pixel.

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Agricultural Meteorology

            A branch of meteorology that examines the effects and impacts of weather and climate on crops, rangeland, livestock, and various agricultural operations. The branch of agricultural meteorology dealing with atmospheric-biospheric processes occurring at small spatial scales and over relatively short time periods is known as micrometeorology, sometimes called crop micrometeorology for managed vegetative ecosystems and animal biometeorology for livestock operations. The branch that studies the processes and impacts of climatic factors over larger time and spatial scales is often referred to as agricultural climatology.

            Agricultural meteorology, or agrometeorology, addresses topics that often require an understanding of biological, physical, and social sciences. It studies processes that occur from the soil depths where the deepest plant roots grow to the atmospheric levels where seeds, spores, pollen, and insects may be found. Agricultural meteorologists characteristically interact with scientists from many disciplines.

          Agricultural meteorologists collect and interpret weather and climate data needed to understand the interactions between vegetation and animals and their atmospheric environments.

            The climatic information developed by agricultural meteorologists is valuable in making proper decisions for managing resources consumed by agriculture, for optimizing agricultural production, and for adopting farming practices to minimize any adverse effects of agriculture on the environment. Such information is vital to ensure the economic and environmental sustainability of agriculture now and in the future. Agricultural meteorologists also quantify, evaluate, and provide information on the impact and consequences of climate variability and change on agriculture. Increasingly, agricultural meteorologists assist policy makers in developing strategies to deal with climatic events such as floods, hail, or droughts and climatic changes such as global warming and climate variability.

           Agricultural meteorologists are involved in many aspects of agriculture, ranging from the production of agronomic and horticultural crops, trees, and livestock to the final delivery of agricultural products to market. They study the energy and mass exchange processes of heat, carbon dioxide, water vapor, and trace gases such as methane, nitrous oxide, and ammonia, within the biosphere on spatial scales ranging from a leaf to a watershed and even to a continent.

            They study, for example, the photosynthesis, productivity, and water use of individual leaves, whole plants, and fields. They also examine climatic processes at time scales ranging from less than a second to more than a decade.

            Agricultural Climatology

            In general, the study of climate as to its effect on crops; it includes, for example, the relation of growth rate and crop yields to the various climatic factors and hence the optimum and limiting climates for any given crop. Also known as agroclimatology.


            A branch of meteorology and ecology that deals with the effects of weather and climate on plants, animals, and humans. The principal problem for living organisms is maintaining an acceptable thermal equilibrium with their environment. Organisms have natural techniques for adapting to adverse conditions. These techniques include acclimatization, dormancy, and hibernation, or in some cases an organism can move to a more favorable environment or microenvironment. Humans often establish a favorable environment through the use of technology.


            The scientific study of climate. Climate is the expected mean and variability of the weather conditions for a particular location, season, and time of day. The climate is often described in terms of the mean values of meteorological variables such as temperature, precipitation, wind, humidity, and cloud cover. A complete description also includes the variability of these quantities, and their extreme values. The climate of a region often has regular seasonal and diurnal variations, with the climate for January being very different from that for July at most locations. Climate also exhibits significant year-to-year variability and longer-term changes on both a regional and global basis. The goals of climatology are to provide a comprehensive description of the Earth’s climate over the range of geographic scales, to understand its features in terms of fundamental physical principles, and to develop models of the Earth’s climate for sensitivity studies and for the prediction of future changes that may result from natural and human causes.

            Crop Micrometeorology

            The branch of meteorology that deals with the interaction of crops and their immediate physical environment.


            The study of small-scale meteorological processes associated with the interaction of the atmosphere and the Earth’s surface. The lower boundary condition for the atmosphere and the upper boundary condition for the underlying soil or water are determined by interactions occurring in the lowest atmospheric layers. Momentum, heat, water vapor, various gases, and particulate matter are transported vertically by turbulence in the atmospheric boundary layer and thus establish the environment of plants and animals at the surface. These exchanges are important in supplying energy and water vapor to the atmosphere, which ultimately determine large-scale weather and climate patterns. Micrometeorology also includes the study of how air pollutants are diffused and transported within the boundary layer and the deposition of pollutants at the surface.

            In many situations, atmospheric motions having time scales between 15 min and 1 h are quite weak. This represents a spectral gap that provides justification for distinguishing micrometeorology from other areas of meteorology. Micrometeorology studies phenomena with time scales shorter than the spectral gap (time scales less than 15 min to 1 h and horizontal length scales less than 2–10 km). Some phenomena studied by micrometeorology are dust devils, mirages, dew and frost formation, evaporation, and cloud streets.


            An ecosystem is a complete community of living organisms and the nonliving materials of their surroundings. Thus, its components include plants, animals, and microorganisms; soil, rocks, and minerals; as well as surrounding water sources and the local atmosphere. The size of ecosystems varies tremendously. An ecosystem could be an entire rain forest, covering a geographical area larger than many nations, or it could be a puddle or a backyard garden. Even the body of an animal could be considered an ecosystem, since it is home to numerous microorganisms. On a much larger scale, the history of various human societies provides an instructive illustration as to the ways that ecosystems have influenced civilizations.

            Weather Observations

            The measuring, recording, and transmitting of data of the variable elements of weather. In the India the National Data Centre (NDC), a division of the India Meteorological Department (IMD), has as one of its primary responsibilities the acquisition of meteorological information.

            The data are sent by various communication methods to the NDC of IMD. At the Center, the raw data are fed into large computers that are programmed to plot, analyze, and process the data and also to make prognostic weather charts. The processed data and the forecast guidance are then distributed by special National Weather Service systems and conventional telecommunications to field offices, other government agencies, and other stake holders. They in turn prepare forecasts and warnings based on both processed and raw data. A wide variety of meteorological data are required to satisfy the needs of meteorologists, climatologists, and users in marine activities, forestry, agriculture, aviation, and other fields.

            Weather forecasting and prediction

            Processes for formulating and disseminating information about future weather conditions based upon the collection and analysis of meteorological observations. Weather forecasts may be classified according to the space and time scale of the predicted phenomena. Atmospheric fluctuations with a length of less than 100 m (330 ft) and a period of less than 100 s are considered to be turbulent.

            The study of atmospheric turbulence is called micrometeorology; it is of importance for understanding the diffusion of air pollutants and other aspects of the climate near the ground. Standard meteorological observations are made with sampling techniques that filter out the influence of turbulence. Common terminology distinguishes among three classes of phenomena with a scale that is larger than the turbulent microscale: the mesoscale, synoptic scale, and planetary scale.

            The mesoscale includes all moist convection phenomena, ranging from individual cloud cells up to the convective cloud complexes associated with prefrontal squall lines, tropical storms, and the intertropical convergence zone. Also included among mesoscale phenomena are the sea breeze, mountain valley circulations, and the detailed structure of frontal inversions. Most mesoscale phenomena have time periods less than 12 h. The prediction of mesoscale phenomena is an area of active research. Most forecasting methods depend upon empirical rules or the short-range extrapolation of current observations, particularly those provided by radar and geostationary satellites.

            Forecasts are usually couched in probabilistic terms to reflect the sporadic character of the phenomena.

            Since many mesoscale phenomena pose serious threats to life and property, it is the practice to issue advisories of potential occurrence significantly in advance. These “watch” advisories encourage the public to attain a degree of readiness appropriate to the potential hazard. Once the phenomenon is considered to be imminent, the advisory is changed to a “warning,” with the expectation that the public will take immediate action to prevent the loss of life.

            The next-largest scale of weather events is called the synoptic scale, because the network of meteorological stations making simultaneous, or synoptic, observations serves to define the phenomena. The migratory storm systems of the extratropics are synoptic-scale events, as are the undulating wind currents of the upper-air circulation which accompany the storms. The storms are associated with barometric minima, variously called lows, depressions, or cyclones. The synoptic method of forecasting consists of the simultaneous collection of weather observations, and the plotting and analysis of these data on geographical maps. An experienced analyst, having studied several of these maps in chronological succession, can follow the movement and intensification of weather systems and forecast their positions. This forecasting technique requires the regular and frequent use of large networks of data.

            Planetary-scale phenomena are persistent, quasistationary perturbations of the global circulation of the air with horizontal dimensions comparable to the radius of the Earth. These dominant features of the general circulation appear to be correlated with the major orographic features of the globe and with the latent and sensible heat sources provided by the oceans. They tend to control the paths followed by the synoptic-scale storms, and to draw upon the synoptic transients for an additional source of heat and momentum.

            Numerical weather prediction is the prediction of weather phenomena by the numerical solution of the equations governing the motion and changes of condition of the atmosphere. Numerical weather prediction techniques, in addition to being applied to short-range weather prediction, are used in such research studies as air-pollutant transport and the effects of greenhouse gases on global climate change.

            The first operational numerical weather prediction model consisted of only one layer, and therefore it could model only the temporal variation of the mean vertical structure of the atmosphere.

            Computers now permit the development of multilevel (usually about 10–20) models that could resolve the vertical variation of the wind, temperature, and moisture. These multilevel models predict the fundamental meteorological variables for large scales of motion. Global models with horizontal resolutions as fine as 125 mi (200 km) are being used by weather services in several countries. Global numerical weather prediction models require the most powerful computers to complete a 10-day forecast in a reasonable amount of time.

            Research models similar to global models could be applied for climate studies by running for much longer time periods. The extension of numerical predictions to long time intervals (many years) requires a more accurate numerical representation of the energy transfer and turbulent dissipative processes within the atmosphere and at the air-earth boundary, as well as greatly augmented computing-machine speeds and capacities.

            Long-term simulations of climate models have yielded simulations of mean circulations that strongly resemble those of the atmosphere. These simulations have been useful in explaining the principal features of the Earth’s climate, even though it is impossible to predict the daily fluctuations of weather for extended periods. Climate models have also been used successfully to explain paleoclimatic variations, and are being applied to predict future changes in the climate induced by changes in the atmospheric composition or characteristics of the Earth’s surface due to human activities.

            Surface meteorological observations are routinely collected from a vast continental data network, with the majority of these observations obtained from the middle latitudes of both hemispheres. Commercial ships of opportunity, military vessels, and moored and drifting buoys provide similar in-place measurements from oceanic regions. Information on winds, pressure, temperature, and moisture throughout the troposphere and into the stratosphere is routinely collected from (1) balloon-borne instrumentation packages (radiosonde observations) and commercial and military aircraft which sample the free atmosphere directly; (2) ground-based remote-sensing instrumentation such as wind profilers (vertically pointing Doppler radars), the National Weather Service Doppler radar network, and lidars; and (3) special sensors deployed on board polar orbiting or geostationary satellites. The remotely sensed observations obtained from meteorological satellites have been especially helpful in providing crucial measurements of areally and vertically averaged temperature, moisture, and winds in data-sparse (mostly oceanic) regions of the world. Such measurements are necessary to accommodate modern numerical weather prediction practices and to enable forecasters to continuously monitor global storm (such as hurricane) activity.

            Forecast products and forecast skill are classified as longer term (greater than 2 weeks) and shorter term. These varying skill levels reflect the fact that existing numerical prediction models such as the medium-range forecast have become very good at making large-scale circulation and temperature forecasts, but are less successful in making weather forecasts. An example is the prediction of precipitation amount and type given the occurrence of precipitation and convection.

            Each of these forecasts is progressively more difficult because of the increasing importance of mesoscale processes to the overall skill of the forecast.

            Nowcasting is a form of very short range weather forecasting. The term nowcasting is sometimes used loosely to refer to any area-specific forecast for the period up to 12 h ahead that is based on very detailed observational data. However, nowcasting should probably be defined more restrictively as the detailed description of the current weather along with forecasts obtained by extrapolation up to about 2 h ahead. Useful extrapolation forecasts can be obtained for longer periods in many situations, but in some weather situations the accuracy of extrapolation forecasts diminishes quickly with time as a result of the development or decay of the weather systems.

            Forecasts of time averages of atmospheric variables, for example, sea surface temperature, where the lead time for the prediction is more than 2 weeks, are termed long-range or extended-range climate predictions. Extended-range predictions of monthly and seasonal average temperature and precipitation are known as climate outlooks. The accuracy of long-range outlooks has always been modest because the predictions must encompass a large number of possible outcomes, while the observed single event against which the outlook is verified includes the noise created by the specific synoptic disturbances that actually occur and that are unpredictable on monthly and seasonal time scales. According to some estimates of potential predictability, the noise is generally larger than the signal in middle latitudes.

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The future of precision agriculture

Using predictive weather analytics to feed future generations

By 2050, it’s expected that the world’s population will reach 9.2 billion people, 34 percent higher than today. Much of this growth will happen in developing countries like Brazil, which has the largest area in the world with arable land for agriculture. To keep up with rising populations and income growth, global food production must increase by 70 percent in order to be able to feed the world.

For IBM researcher and Distinguished Engineer Ulisses Mello and a team of scientists from IBM Research – Brazil, the answer to that daunting challenge lies in real time data gathering and analysis. They are researching how “precision agriculture” techniques and technologies can maximize food production, minimize environmental impact and reduce cost.

“We have the opportunity to make a difference using science and technological innovation to address critical issues that will have profound effect on the lives of billions of people,” said Ulisses.

Optimizing planting, harvesting and distribution

In order to grow crops optimally farmers need to understand how to cultivate those crops in a particular area, taking into account a seed’s resistance to weather and local diseases, and considering the environmental impact of planting that seed. For example, when planting in a field near a river, it’s best to use a seed that requires less fertilizer to help reduce pollution.

Once the seeds have been planted, the decisions made around fertilizing and maintaining the crops are time-sensitive and heavily influenced by the weather. If farmers know they’ll have heavy rain the next day, they may decide not to put down fertilizer since it would get washed away. Knowing whether it’s going to rain or not can also influence when to irrigate fields. With 70 percent of fresh water worldwide used for agriculture, being able to better manage how it’s used will have a huge impact on the world’s fresh water supply.

Weather not only affects how crops grow, but also logistics around harvesting and transportation. When harvesting sugar cane, for example, the soil needs to be dry enough to support the weight of the harvesting equipment. If it’s humid and the soil is wet, the equipment can destroy the crop. By understanding what the weather will be over several days and what fields will be affected, better decisions can be made in advance about which fields workers should be deployed to.

Once the food has been harvested the logistics of harvesting and transporting food to the distribution centers is crucial. A lot of food waste happens during distribution, so it’s important to transport the food at the right temperature and not hold it for longer than needed. Even the weather can affect this; in Brazil, many of the roads are dirt, and heavy rain can cause trucks to get stuck in mud. By knowing where it will rain and which routes may be affected, companies can make better decisions on which routes will be the fastest to transport their food.

The future of precision agriculture

Currently, precision agriculture technologies are used by larger companies as it requires a robust IT infrastructure and resources to do the monitoring. However, Ulisses envisions a day when smaller farms and co-ops could use mobile devices and crowd sourcing to optimize their own agriculture.

“A farmer could take a picture of a crop with his phone and upload it to a database where an expert could assess the maturity of the crop based on its coloring and other properties. People could provide their own reading on temperature and humidity and be a substitute for sensor data if none is available,” he said.

With growing demands on the world’s food supply chain, it’s crucial to maximize agriculture resources in a sustainable manner. With expertise in high performance supercomputing, computational sciences, and analytics and optimization, IBM Research is uniquely able to understand the complexities of agriculture and develop the right weather forecasts, models and simulations that enable farmers and companies to make the right decisions.

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Effective micro-organisms for ecological agriculture during transition

About 25 years ago, I came to know about Effective Microorganisms and their use in agriculture, animal health and sanitation through a Japanese friend who visited my farm and also arranged to get literature about Effective Microorganisms. Prof. Teruo Higa, an agronomist, modified an age-old Japanese technology which he learnt from his grandmother. Traditionally, Japanese farmers used to make ‘Bokashi’, a concentrated form of  compost, apply it to the soil along with other organic manures. The purpose was to inoculate beneficial organisms to improve the quality of organic manure and to check fungus and virus problems in the  soil. They used to collect chemical free soil, rich in humus, from forests and mix it with dry cow dung powder, dry fish meal, jaggery syrup, oil cake and rice bran, adding about 10% to 12% of potable water. The anaerobic compost thus prepared was used at the rate of 100 grams per square metre of land. Prof. Higa, further worked on this traditional practice along with his friend, a microbiologist and introduced Effective Microorganisms to agriculture, animal health and sanitary uses. Now, almost after 30 years of its introduction, it is being used in most of the countries all over the world. In India, through its licensed tie-up with Maple Orgtech (I) Limited, the Effective Microorganisms are being supplied through their distributors all over India.

What is EM?

EM contains more than 70 beneficial organisms, more importantly  lactic acid bacteria, photosynthetic bacteria (Rhodopseudomonas Palustris) and yeast. Surprisingly, use of EM helps in augmenting the photosynthesis by about 30% in all the crops. Further, it controls viruses and fungal damage to crops and animals by inoculating lactic acid bacteria and actinomycetis bacteria. It is very expensive and not very effective to use the stock solution. So, the farmer has to prepare Secondary Effective Microorganisms (SEM) or Extended Effective Microorganisms (EEM).

To prepare SEM/EEM, we need a 20 litre plastic can, free from chemicals, 20 litres of potable water (not chlorinated, or bleaching power being used for purification), 1 or 2 kgs of chemical free Jaggery. Mix jaggery in 20 litres of water in the plastic can and add one litre of Effective Microorganisms stock solution. Close the lid and keep in a cool and dark place for about 8 to 10 days. The PH will come down to 3.5 and the processed product – E.E.M or S.E.M will smell sweet and sour like a mixture of jaggery and curd.

Ways in which EEM can be used

E.E.M or S.E.M can be used in agriculture in 5 ways.

1. Direct use of E.E.M

You can spray E.E.M. directly on crops at 0.1% or one ml in one litre of water. You can also spray on the soil or crop residues at 0.5% to help them break down much faster (particularly sugarcane and paddy thrash). If you have S.E.M in excess, not being used after 60 days, you can spray at 0.5% on your compost heap.

2. Enriched Urine with E.E.M

Collect urine including human urine and process anaerobically for 8 days. Mix 50 ml E.E.M with one litre of urine and 100 gms of jaggery and spray on crops at the rate of one ml in one litre of water. Farmers in Doddaballapura, Bangalore Rural district, Karnataka State area are collecting urine from school latrines and are using on their crops as soil application as they hesitate to spray on crops. But for sure there will be no traces of bad odour after addition of E.E.M and fermentation done anaerobically.

3. Fermented Plant Extraction (F.P.E)

Collect about 10 kgs of weeds at the time of sunrise and cut them into 2 inch pieces. Fill them into a plastic container with water, adding 500ml of E.E.M. and 500 ml of jaggery syrup. Close the lid, not too tight, as this particular fermentation releases some gas. Allow it to ferment for 8 days, in a cool and dark place. You will find clear odourless liquid which can be strained in a cotton cloth. This sap can be sprayed on the crops at one ml in one litre of water i.e., at the rate of 0.1%.

4. Bokashi or concentrated compost

You need 100 litres of fine rice or wheat bran, 10 kgs of dry cow dung powder, 10 kgs of groundnut oil cake, 5 kgs dry fish meal, 2 kgs of jaggery, about 12 to 14 litres of chemical free potable water, one litre of SEM or EEM and a suitable plastic container to fill all the above material. Mix all the ingredients well and fill into the container as tightly as possible for anerobic composting for 8 to 10 days in a cool and dark place. The pH will come down below 3.5 and the product can be mixed with soil at a cooler time along with other organic manures at the rate of 100 gms per square metre.

5. E.M. 5

You will need 600 ml of chemical free potable water, 100 ml of jaggery syrup, 100 ml of E.E.M or S.E.M, 100 ml of ethyl alcohol (rum or brandy) and 100 ml of natural vinegar. Fill and mix all the above ingredients in 1 litre bottle and allow to ferment anerobically in a cool and dark place for 8 to 10 days.

The pH will come down to 3.5. You can spray EM 5 as an antifungal, antiviral and insecticide at the rate of one ml in one litre of water. In my vast experience on my family’s five mixed (bio-intensive) farms, I can recommend the use of EM to increase soil fertility and suppress development of harmful organisms. In the first two to three years, we used EM as a 5 percent spray on our crop residues such as maize, rice paddy stubble and sunflower, to decompose them quickly. We noticed that by using EM spray, composting is quicker and better. Similarly, when we applied bokashi (another EM product) together with farmyard manure, we noticed that our rice, tomato, bottlegourd, soyabean, gladiolus, banana and papaya crops were free from fungal attacks and viral diseases. Another EM preparation was very useful in controlling sucking insects on legumes and cucurbits. We have observed better growth in the leaves and stems of crops sprayed with different EM preparations, leading to yield increases of 15 percent and fewer pest infestations.

Farmers in Erode District of Tamil Nadu in South India, are regularly using EM preparations for soil treatment to check root-rots. Farmers in Raichur District, Karnataka State are using EM to help quicken the breakdown of paddy stubble, as do sugarcane growers in Sivaganga District, Tamil Nadu. The EPPL thermal power company, with 700 acres of hill neem trees (also in Tamil Nadu), found that the germination capacity of their seeds increased from 5 percent at the beginning to 85 percent after soaking their dry fruits in 5 percent EM solution for 24 hours before planting. I myself and over 500 farmers in the area also use EM solution to soak all our seeds before  sowing.

Care in use of EM

Since Effective Microorganisms are basically an inoculum of beneficial organisms, care needs to be taken not to use any chemicals in the same land. Also, as these are acidic in nature, EM preparations of 0.1% only should be sprayed, otherwise, it may scorch the plants. All the preparations have to be stored in a cool and dark place and should be used before 60 to 70 days of preparation.

Although some farmers produce their own micro-organism mixtures, for example, keeping rice gruel near humus rich wet soil for 4-5 days, my fear is that farmers cannot identify any harmful organisms getting into the preparations, as they do not have suitable laboratory equipment to segregate them. Therefore, I think it is better to get EM stock solution from an authentic laboratory. It is very cheap to use it; in India, the use of EM on one acre costs less than a cup of coffee. Farmers use it 3-4 times a year on all their crops. Nevertheless, it is enough to use EM preparations only in the first 2-3 years during the transition from chemical to organic  farming. It is very useful in building up the population of beneficial

organisms both in the soil and plants. In my opinion, use of EM is the best way for farmers intending for a transition from chemical farming to bio-intensive farming.

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Open System for Organic Agriculture Administration

Efforts to increase the availability of sustainable development in natural resources worldwide are  consecutive and proliferated through the last decades. Sectors and divisions of many scientific  networks are working simultaneously in separate schemas or in joined multitudinous projects and  international co-operations. Organic Agriculture, as a later evolution of farming systems, was  derived from trying to overcome the accumulative environmental and socioeconomic problems of  industrialized communities and shows rapid development during the last decades. Its products  day to day gain increased part of consumer preferences while product prices are rather higher  than those of the traditional agriculture. Governments all over the world try to reduce the  environmental effects of the industrialized agriculture, overproduction and environmental  pollution, encouraging those who want to place their fields among others that follow the rules  of organic agriculture. All the above make this new trend very attractive and promising.

But the rules in organic agriculture are very restrictive. The intensive pattern of cultivation  worldwide and the abuse of chemical inputs, affected the environment, therefore any field  expected to be cultivated under the rules of organic agriculture has to follow certain steps but  also be ‘protected’ from the surrounding plots controlling at the same time different kind of unexpected influx (e.g., air contamination from nearby insecticides’ use, water pollution of  irrigation system from an adjacent plot that has used fertilizers, etc). It is obvious that the gap  between wish and theory and the implementation of organic agriculture is enormous.

Obviously one can overcome this gap using a sophisticated complex system. Such a system  can be based on a powerful GIS and the use of widely approved mobile instruments for  precise positioning and wireless communication. In such a system data-flow could be an  “easy” aspect, providing any information needed for the verification of organic product cycle  at any time, any site. 


As the world’s population has increased from 1.6 billion at the beginning of the 20th century  to over 6.2 billion just before the year 2004, economic growth, industrialization and the demand for agricultural products caused a sequence of unfortunate results. This aggregation of disturbances moved along with the reduction in availability and deterioration of maximum yield results from finite ranges of plots on earth’s surface. Overuse of agrochemical products (insecticides, pesticides, fertilizers, etc.), reduction and destruction of natural resources, decrease of biodiversity, reduction of water quality, threat over rare natural landscapes and wild species and an overall environmental degradation, appeared almost daily in news worldwide especially over the last two decades. The universal widespread of this situation has raised worldwide awareness of the need for an environmentally sustainable economic development. (WCED, 1987) In the beginning of year 2004, EU Commission for Agriculture, Rural Development and Fisheries declared three major issues towards a European Action Plan on organic food and farming that may be crucial for the future of organic agriculture:

− the market, (promotion and distribution)

− the role of public support and,

− the standards of organic farming.

It is obvious that in general the market has a positive reaction if there is a prospect of considerable gain. Thus we can say that the other two will define the future. The strict rules of organic agriculture have to be ensured and all the products have to be easily recognisable.Also a guarantee about the quality and the origin of any product has to be established.

Organic Farming is derived as a sophisticated sector of the evolution of farming implementation techniques aiming through restrictions and cultivated strategies to achieve a balanced production process with maximum socioeconomic results (better product prices, availability of surrounding activities as ecotourism, family employment in low populated villages, acknowledge of natures’ and rural environments’ principles and needs, etc.). Meanwhile, the combination of latest technological advances, skills, innovations and the decline of computer and associate software expenses were transforming the market place of geographic data. Now, more than ever before, common people, farmers, private enterprises, local authorities, students, researchers, experts from different scientific fields, and a lot more could become an important asset supporting the development of innovations of Informatics in Geospatial Analysis. With the use of Geographic Information Systems and Internet applications various data can be examined visually on maps and analyzed through geospatial tools and applications of the software packages. Much recent attention and efforts has been focused on developing GIS functionality in the Worldwide Web and governmental or private intranets. The new applicable framework, called WebGIS, is surrounded with a lot of challenges and is developed rapidly changing from day to day the view of contemporary GIS workstations.

Precision Organic Agriculture through GIS fulfils the demands of design strategies and managerial activities in a continuing process. By implementing this combination, certified methods for defining the best policies, monitoring the results and the sustainability of the framework, and generating a constructive dialogue for future improvement on environmental improvement and development could be developed.


Organic Agriculture is derived from other organized smaller natural frameworks, publicly known as ecosystems which are complex, self-adaptive units that evolve through time and natural mechanisms and change in concern with external biogeochemical and natural forces.

Managing ecosystems should have been focused on multiplication of the contemporary needs and future perspectives to ameliorate sustainable development. Instead, political, economic and social agendas and directives, as well as scientific objectives resulted in few decades such an enormous amount of global environmental problems like never before in the history of mankind. Valuable time was spent over the past 75 years by research, which was trying to search how ecosystems regulate themselves, for example how they adjust to atmospheric, geologic, human activities and abuse (Morain, 1999).

Organic Agriculture flourished over the last decade particularly after 1993 where the first act of Regulation 2092/91 of European Union was enforced. Until then, and unfortunately, afterwards, worldwide environmental disasters ( e.g., the Chernobyl accident of the nuclear reactor in April 1986), accumulative environmental pollution and its results (acid rain, ozone’s hole over the Poles, Greenhouse effect, etc.) and even lately the problems that occurred by the use of dioxins and the propagation of the disease of “mad cows”, increase in public opinion the relation between natures’ disturbances and the continuing abuse of intensive methods of several industrialized chains of productions. Among them, conventional agricultural intensive production with the need of heavy machinery, enormous needs of energy consumptions and even larger thirst for agrochemical influxes the last fifty years, created environmental disturbances for the future generations. Therefore, IFOAM (International Federation of Organic Agriculture Movements) constituted a number of principles that, enabling the implementation of Organic Farming’s cultivation methods, techniques and restrictions worldwide.

Principles of Organic Agriculture Organic Agriculture:  (Source: IFOAM)

  •  aims on best soil fertility based in natural processes,
  •  uses biological methods against insects, diseases, weeds,
  • practices crop rotation and co-cultivation of plants
  • uses “closed circle” methods of production where the residues from former cultivations or other recyclable influx from other sources are not thrown away, but they are incorporated, through recycling procedures, back in the cultivation (use of manure, leaves, compost mixtures, etc.),
  • avoids heavy machinery because of soil’s damages and destruction of useful soil’s microorganisms,
  • avoids using chemicals,  avoids using supplemental and biochemical substances in animal nourishing,
  •  needs 3-5 years to transit a conventional cultivated field to a organic farming system following the restrictions of Council Regulation (EEC) No 2092/91,
  • underlies in inspections from authorities approved by the national authorities of Agriculture.

An appropriate organic plot should be considered as the landscape where ecological perspectives and conservations activities should be necessary for effective sustainable nature resource management (Hobs, 1997). Considerable amounts of time and effort has been lost from oncoming organic farmers on finding the best locations for their plots. Spatial restrictions for placing an organic farm require further elaboration of variables that are affecting cultivation or even a unique plant, such as:

− Ground-climatic variables (e.g., ground texture, ph, slope, land fertility, history of former yields, existence of organic matter, rain frequency, water supply, air temperature levels, leachability, etc.),

− Adjacency with other vegetative species (plants, trees, forests) for propagation reasons or non-organic cultivations for better controlling movements through air streams or erosion streams (superficial or in the ground) of agrochemical wastes,

− Availability of organic fertilization source from neighbored agricultural exploitations,

− Quality of accessing road network for agricultural (better monitoring) and marketing (aggregated perspectives of product distribution to nearby or broadened market area) reasons.

A GIS is consisted of computerized tools and applications that are used to organize and display geo-information. Additionally it enables spatial and non-spatial analysis and correlation of geo-objects for alternative management elaborations and decision making procedures. This gives the ability to GIS users or organic farm-managers to conceive and implement alternative strategies in agricultural production and cultivation methodology.


The development of first concepts and ideas of a precision organic farming system in a microregion, demands a regional landscape qualitative and recovery master plan with thorough and comprehensive description of the territory (land-use, emission sources, land cover, microclimatic factors, market needs and other essential variables. Essential components on a successful and prospective organic GIS-based system should be:

− The time-schedule and task specification of the problems and needs assessments that the design-strategy is intended to solve and manage,

− Integrated monitoring of high risks for the cultivation (insects, diseases, water quality, water supply, weather disturbances (wind, temperature, rain, snow, etc.),

− Supply of organic fertilization because additional needs from plants in certain periods of cultivation could be not managed with fast implemented agrochemicals; instead they need natural fermentations and weather conditions to break down elements of additional fertilization,

− High level of communication capabilities with authorized organizations for better management of the cultivation and geodata manipulation, aiming on better promotional and economical results,

− Increased awareness of the sustainability of the surrounding environment (flora and fauna), enabling motivation for a healthy coexistence. For example, the conservation of nearby natural resources such as rare trees, small bushes and small streams, give nest places and water supply capabilities to birds and animals that help organic plants to deal with insect populations controls and monitoring of other plant enemies,

− Continual data capture about land variables, use of satellite images, georeference  sampling proccedures and spatial modelling of existed or former geospatial historical plot’s data could be used to establish a rational model which will enable experts and organic farmers to transform the data into supportive decision applications.

The combination and modeling of all necessary variables through any kind of methodological approach, could be achieved through GIS expressing the geographical sectors of land parcels either as a pattern of vector data, or as a pattern of raster data (Kalabokidis et al., 2000). Additionally, we could allocate the cultivation or the combination of cultivations1 and their units (plants, trees, etc.) so as to be confronted in relation with their location inside the field, as well as with the neighbored landscape. For this purpose the most essential tool would be a GPS (Global Positioning System) device with high standards of accuracy. Several statistical approaches and extensions have been developed for the elaboration of spatial variables through geostatistical analysis. The usefulness of these thematic maps lies upon the tracing and localization of spatial variability in the plot during the cultivated period, enabling the farmer to implement the proper interferences for better management and future orientation of the farm and of the surrounding area.

Specific geodata receivers and sensors inside the plot, in the neighbored area, as well as images from satellites, could establish a “temporal umbrella” of data sources of our farm which would submit in tracing of temporal variability factors in our field. The agricultural management framework that takes into account the spatial or temporal variability of different parameters in the farm is called Precision Agriculture (Karydas, et al., 2002). The implementation of IFOAM’s principles in such an agricultural model should be called Precision Organic Agriculture (POA).


The development of appropriate analytical techniques and models in a variety of rapidly changing fields using as cutting edge GIS technology, is a high-demanding procedure. The linkages to different applications of spatial analysis and research and the ability to promote functional and integrated geodatabases is a time consuming, well prepared and carefully executed procedure which combines spatial analytic approaches from different scientific angles: geostatistics, spatial statistics, time-space modeling, mathematics, visualization techniques, remote sensing, mathematics, geocomputational algorithms and software, social, physical and environmental sciences.

An approach of a Precision Organic Farming model, which uses as a structure basis the Precision Agriculture wheel (McBratney et al., 1999) and the introduction of organic practices for the sustainable development with the elaboration of any historical data about the plot. The basic components are:

− Spatial referencing: Gathering data on the pattern of variation in crop and soil parameters across a field. This requires an accurate knowledge of allocation of samples and the GPS network.

− Crop & soil monitoring: Influential factors effecting crop yield, must be monitored at a thoroughly. Measuring soil factors such as electric conductivity, pH etc., with sensors enabling real-time analysis in the field is under research worldwide with focusing on automation of results. Aerial or satellite photography in conjunction with crop scouting is becoming more available nowadays and helps greatly for maximizing data acquisition for the crop.

− Spatial prediction & mapping: The production of a map with thematic layers of variation in soil, crop or disease factors that represents an entire field it is necessary to estimate values for unsampled locations.

− Decision support: The degree of spatial variability found in a field with integrated data elaboration and quality of geodata inputs will determine, whether unique treatment is warranted in certain parts. Correlation analysis or other statistical approaches can be used to formulate agronomically suitable treatment strategies.

− Differential action: To deal with spatial variability, operations such as use of organic-“friendly”-fertilizers, water application, sowing rate, insect control with biological practices, etc. may be varied in real-time across a field. A treatment map can be constructed to guide rate control mechanisms in the field.

GIS systems from their beginning about than 30 years ago, step by step, started to progress from small applications of private companies’ needs to high demanding governmental applications. At the beginning, the significance and capabilities of GIS were focusing on digitizing data; today, we’ve reached the last period of GIS’s evolution of data sharing. Nowadays restrictions and difficulties are not upon the hardware constraints but they are on data dissemination. Several initiatives have been undertaken in order to provide basic standard protocols for overcoming these problem. The need of organisational and institutional cooperation and establishment of international agreement framework becomes even more important. Governments, scientific laboratories, local authorities, Non Governmental Organizations (NGOs), private companies, international organizations, scientific societies and other scientific communities need to find substantial effort to broaden their horizons through horizontal or vertical standards of cooperation.

Any GIS laboratory specialized in monitoring a specific field could give additional knowledge to a coherent laboratory which focus to an other field in the same area. As a result, especially in governmental level, each agency performs its own analysis on its own areas, and with minimal effort cross-agency interactions could increase the efficiency of projects that help the framework of the society.

Such a data-sharing framework was not capable in earlier years, where technological evolution was trying specific restrictions of earlier operational computerised disabilities. Hardly managed and high demanding knowledge in programming applications, unfriendly scheme of computer operating systems over large and expensive programs, and restricted knowledge on Internet applications now belong to the past. User friendly computer operation systems, high storage capacity, fast CPUs (Central Processing Units) sound overwhelming even in relation with PCs before ten years. Powerful notebooks, flexible and strong PDAs, super-computers of enormous capabilities in data storage, true-colour high resolution monitors and other supplementary portable or stable devices, created an outburst in the applications of Information Technology (IT). Additionally, the expansion of Internet in the ‘90s worldwide, contributed (and is still keeping on doing this) on redesigning specific applications for data mining procedures through WWW (World Wide Web), as well as for data exporting capabilities and maps distribution through Internet. The evolution in computer software derived new versions of even friendlier GIS packages.


The Internet as a system followed an explosive development during the past decade. The modern Internet functions are based on three principles (Castells, 2001):

 − Decentralized network structure where there is no single basic core that controls the whole system.

− Distributed computing power throughout many nodes of the network.

− Redundancy of control keys, functions and applications of the network to minimize risk of disruption during the service.

Internet is a network that connects local or regional computer networks (LAN or RAN) by using a set of communication protocols called TCP/IP (Transmission Control Protocol/Internet Protocol). Internet technology enables its users to get fast and easy access to a variety of resources and services, software, data archives, library catalogs, bulletin boards, directory services, etc. Among the most popular functions of the Internet is the World Wide Web (WWW). World Wide Web is very easy to navigate by using software called browser, which searches through internet to retrieve files, images, documents or other available data.

The important issue here is that the user does not need to know any software language but all it needs is a simple “click” with mouse over highlighted features called Hyperlinks, giving  increased expansion on growth of WWW globally.

GIS data related files (Remote Sensing data, GPS data, etc) can benefit from globalization of World Wide Web:

− An enormous amount of these data are already in PC-format.

− GIS users are already familiar by using software menus.

− Large files could be easily transmitted through Internet and FTPs and software about compression.

− The Web offers user interaction, so that a distant user can access, manipulate, and display geographic databases from a GIS server computer.

− It enables tutorials modules and access on educational articles.

− It enables access on latest achievements in research of GIS through on-line proceedings of seminars, conferences, etc.

− Through Open Source GIS, it enables latest implementations of GIS programming and data sharing by minimum cost.

− Finally through online viewers, it gives the capability of someone with minimum  knowledge on GIS to get geospatial information by imaging display. (Aber, 2003)

The importance of World Wide Web could become more crucial through wireless Internet access. For a GIS user who works on the street, or in our case, on the field of an organic farm and uses wireless access to the web, a GIS package through a portable device, data transmission is an important issue. This is more important especially if the data are temporalaffected (e.g., meteorological data). To overcome this problem, new data transmission methods need to be elaborated and used in web-based GIS systems to efficiently transmit spatial and temporal data and make them available over the web. Open Source GIS through Internet represents a cross-platform development environment for building spatially enabled functions through Internet applications. Combinations of freely available software through WWW (e.g., image creation, raster to vector, coordinates conversion, etc), with a  combination of programming tools available for development of GIS-based applications could provide standardized geodata access and analytical geostatistical tools with great diplay efficiency. Under this framework, several geospatial applications can be developed using existing spatial data that are available through regional initiatives without costing anything to the end user of this Open GIS System (Chakrabarti et al., 1999).


As the World Wide Web grew rapidly, sophisticated and specialized methods for seeking and organizing data information have been developed. Powerful search engines can be searched by key words or text phrases. New searching strategies are under development where web links are analyzed in combination with key words or phrases. This improves the effectiveness at seeking out authoritative sources on particular subjects. (Chakrabarti et al., 1999) Digital certification under international cooperatives and standards is fundamental for the development of organic agriculture in general and particularly in the market framework. Based on the theory of “dot per plot” different functional IDs could be created under password protected properties through algorithm modules. This way, a code bar (like those on products in supermarkets) could be related through GIS by farmers ID, locations ID, product ID, parcel ID and could follow this product from organic plot to market places giving all the details about it. Even more, authorization ID could be established this way for controlling even the farmer for cultivated methods undertaken in the field that are underlie EUs’ legislations and directives.

 In many cases the only way to create or maintain a separate “organic market” is through certification which provides several benefits (Raghavan, et al., 2002):

− Production planning is facilitated through indispensable documentation, schedules, cultivation methods and their development, data acquisition (e.g., lab results on soil’s pH, electrical conductivity, organic conciseness, etc.) and general production planning of the farm − Facilitation of marketing, extension and GIS analysis, while the data collected in the process of certification can be very useful as feedback, either for market planning, or for extension, research and further geospatial analysis.

− Certification can facilitate the introduction of special support schemes and management scenarios for organic agriculture, since it defines a group of producers to support.

− Certification tickets on products under international standards improve the image of organic agriculture in the society as a whole and increases the creditability of the organic movement.

Because a certification ticket is not recognised as a guarantee standard by itself, the level of control system in biological farming is quite low. In Greece, we are familiar with farmers having a bench by the road and using hand made tickets for their products, they call them “biologic” aiming in higher prices. Marketing opportunities for real organic farmers are eliminating while at the same time EU is trying to organize the directives for future expansion of organic agriculture.

Designing a functional infrastructure of a Geodatabase, fully related with Internet applications, requires accumulative levels of modular mainframes that could be imported, managed and distributed through WWW applications. The security and reliability of main GIS databases have to be established and confirmed through international standards (ISOs) and authorized GIS packages and users as well as in relation with governmental agencies. On the next level, additional analysis of geodata files and agricultural related information data should be combined and further elaborated. For the base level, fundamental GIS functions and geodata digitization should be implemented through internetic report applications (HTML reports, site-enabled GIS, wireless GIS applications, etc.). By this framework we could create a data base where using any ID number (farmer, product, field, etc) will be easy to recognize the history of any specific item involved in the life cycle of the organic farming through a data-related link over thematic maps by GIS viewers in the Internet. Although this framework is supported by multifunctional operations, we could distinguish sectors with homogeneity features:

In the first level of accessing an Open GIS Web system, the users should be first able to access the system through a Web browser. Free access should be available here for users who want to retrieve information, as well for users who want to login for further, more advanced queries. Fundamental GIS functions and geodata digitization should be implemented through internetic report applications (HTML reports, site-enabled GIS, wireless GIS applications, etc.). In this level public participation is enabled through importing additional geodata sets and any other kind of information resources (for example, latest weather information, market demands, research accomplishments, latest equipment facilities, personal extensions for GIS packages, etc.). The eligibility of these data should be applied after studying standards criteria in the next level by experts. Technological advances are also providing the tools needed to disseminate real-time data from their source to the web mapping services, available to the users through the Internet, portable devices, cellular telephones, etc. Basic field work for agricultural and Remote Sensing purposes, as well as data gathering for further statistical analysis should be implemented. By this level, the user could access the system through browsing commands or hyperlinks and through GIS queries. The significant point here is that the access is completely free for anyone who wants to retrieve information but classified to everyone who wants to submit any kind of information by the meaning that he has to give either a user’s ID or personal details.

The second level of accessing the system , is the authorized expert’s level. Here additional analysis of geodata files and agricultural related information data should be combined and further elaborated. Expert analysts from different scientific fields (GIS, economists, topographers, agriculturists, ecologists, biologists, research, etc.) are “bridging” the two levels of the system by using high sophisticated computer tools and GIS packages to facilitate data transportation through WWW channels between clients and servers. In the database file an identity code (IdC) or feature code (FC) is distributed, following the geodata file from main Geodatabase server to the final user. By this framework we could create a data base where using any ID number (farmer, product, field, etc) will be easy to recognize the history of any specific item involved in the life cycle of the organic farming through a data-related link over thematic maps by GIS viewers in the Internet. Additional demand on this level should be considered to be indispensable a background in Web functions with further support by Web experts for adequate Web System Administration.

In the third level of this Web based GIS system,  the success is relying on cooperation between authorized users only. This partnership should be established between geographic information data providers and data management authorities at a governmental, local or private level by authorized personnel. International collaboration could provide even better results in data quality and quantity but requires additional data storage capabilities and special awareness on data interoperability and standards interchange eligibility confirmed through international standards (ISOs). The security of personal details must be followed enriching this level with further authorization controlling tools. The significance of designing successful strategies for case management, using authorized, legitimate GIS packages should also be supported through Web applications and algorithms available for GIS-Web users on global based patterns .


The generally accepted purpose of organic agriculture is to meet the needs of the population and environment of the present while leaving equal or better opportunities for those of the future. Development of this sector is increasing through coordinated activities worldwide by international organizations (EU, UN, FAO, etc.) with long-lasting master plans. The dynamic factor of organic agriculture should not be kept without support. Political initiatives should stand side by side with organic farmers helping them to increase the quality of products and to multiply the number of producers and of the cultivated area.

The accumulative development of Organic Agriculture in Europe needs to be followed by additional development of management activities and strategies in national, binational and international level. Combined actions should be undertaken in fields like telecommunications standards, computer software and hardware development, research projects on agricultural management through GIS, additional educative sectors in universities.

The restrictions that accompany organic farming should help in establishing international agreements that will help to increase the number of qualitative standards, allowing better perspectives for developing future GIS based management strategies. The implementation of an Internet Based Precision Organic Agricultural System requires committed research from the agricultural industry and improvements in geoanalysis, agricultural and information technology. GIS based systems will become more essential as a tool to monitor agricultural exchanges between inputs and outputs and in relation with adjacent regions at an increasingly detailed level. The results will enhance the role of Geographic Information as a functional and economic necessity for any productive community.

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Precision farming is a method of crop management by which  areas of land within a field may be managed with different levels of input depending upon the yield potential of the crop in  that particular area of land. The benefits of so doing are two  fold:

− the cost of producing the crop in that area can be  reduced;

− the risk of environmental pollution from  agrochemicals applied at levels greater than those required by the crop can be reduced.

Precision farming is an integrated agricultural management  system incorporating several technologies. The technological  tools often include the global positioning system GPS,  geographical information system GIS, remote sensing, yield  monitor and variable rate technology.

The paper talks about the use of GPS to support agricultural  vehicle guidance. Equipment for this purpose consists on a  yield monitor installed: the system supports human guide by  means of a display mapping with a GIS the exact direction  produced by GPS receiver put on vehicle top: the driver  follows it to cover in an optimal path the full field.

GPS receivers for this applications require, not only an high accuracy to ensure the reduction of input products, but even an  easy and immediate way of use for farmers; without forgetting low costs. Obviously the technology to achieve high precision still exists but it is too expensive and difficult to use for not skilled people. Survey modality usually adopted in agricultural applications is real time kinematic positioning, DGPS RTK, which enable tohave a good accuracy by means of corrections received. In this experimentation the aim is to obtain a sub-metric accuracy using low cost receivers, which can provide only point positioning. These receivers have been developed for maritime navigation purposes; our aim is their optimization in order to apply them for land navigation in particular for farming activities. Some tests using these receivers were carried out, but results were not satisfying and probably the reason has to be assigned to the implementation of a Kalman filtering inside the receiver software. This is the starting point for a new project, at the moment still in progress, which aim to develop a new  algorithm based on Kalman filter. Its purpose is to improve low cost receiver outputs in order to optimize trajectories and to reach needed accuracy in vehicle positioning during agricultural  activities.


1 Instruments and tests

Experimentation has been carried out using Leica Geosystems  instruments; in particular the low cost receiver discussed in the paper is the TruRover Leica. Its mainly features are: it is an antenna-receiver integrated instrument, it has a 5 Hz tracking time, the report is in the NMEA string format, it cannot neither store positions nor show them in real time, it requires a computer to view NMEA data stream. TruRover performances were compared with geodetic receiver one, which are considerably better, so they are the perfect comparison condition to estimate Trurover positioning quality.

Geodetic receiver used is the GX1230 Leica, able to receive double frequency (both code and phase). Both static and kinematic tests were performed, simulating the typical behaviour of an agricultural vehicle (straight and parallel trajectories with reduced velocity, such as 20÷40 km/h)

and using, at the same time, the two different kinds of GPS receivers described above. At the top of the vehicle, both TruRover and geodetic antenna, connected to the receiver, were placed at a distance of 50 cm. Three static stops with 20 minutes time length were performed, spaced with two steps in motion. Geodetic receiver were set with a 1 second tracking time and a cut off angle of 10 degree. Tests length were about two hours. Another geodetic receiver were placed for a single point positioning and used as the Master station for the following data processing.

2. Data processing

Master station coordinates were determined by means of a static processing in relation to two different GPS permanent station in order to check result: one placed in Modena, where tests have

been carried out, led by INGV and the other located near Bologna, led by ASI Telespazio.

TruRover NMEA data already contain coordinates and Visual GPS software has been utilized to show and store them. These positions have been compared to data stored by double frequency receiver during kinematic tests. These data were utilized to estimate the exact trajectory, which was estimated by the postprocessing in kinematic differential modality. Software for data processing was Leica Geo Office. To be honest this trajectory is not exact because even kinematic postprocessing data have some errors; however this modality has a centimetric accuracy, better than the required from agricultural applications one so it is not a mistake to consider this track as an exact one. TruRover track and the exact one are not yet comparable because 50 cm shift still exists: a kind of overlap has been done by means of setting vehicle motion direction thanks to postprocessed trajectory.

3 Results analysis

The results of the comparison between TruRover track and double frequency receiver one are not satisfying; indeed receivers utilized in experiments show some problems in curves, where the estimated track is larger than the exact one. This bad performance may be due to the presence of a Kalman filter inside the system, that is not optimized for the specific application. Probably at each epoch this filter uses previous estimated positions in order to anticipate the future one on a constant velocity, linear trajectory assumption. In that way when vehicle curves the filter understand it as a mistake and modify the position; this behaviour causes a delay in curving and consequently a shift in positioning.

Higher precision for agricultural applications is not required in curves but in straight directions, where farmers make their main activities on yield. However curves have a great importance mainly at their end because there it is necessary for the vehicle trajectory to be parallel to the previous one. The main reason for that is to economize input products spread about field. Kinematic trajectory is considered the exact one, the reference for a comparison between pseudo-range and kinematic tracks.

The results show distances greater than 1 meter (the target aimed) but always inside the method precision (10 meters). Statistical parameters, as means and standard deviations,  confirm the same things. Table 2 and 3 relate these statistical  valuers. At the beginning the idea was that Kalman filter needs a period of assessment time to work better; on the contrary,  with the elapsed time the differences increase with a worrying time drift. 



The reason for problems in curve is probably the presence of a Kalman filtering inside TruRover, not especially studied for farming applications. Thereof the need of trying a kind of TruRover performances improvement pursued by means of the development and the implementation of a new algorithm based on Kalman filtering and, at the same time, optimized for agricultural requirements.

The first problem was the choice of the process modelling to put in Kalman equations. In particular two trials have been done and described in the following: the constant velocity model and the constant acceleration model. Before the models description, it will be shortly illustrated Kalman filter principles.


The above described problems are a great problem for precision  farming because bad tracks in the field cause wastes of material, without considering economical and environmental impacts. So that, starting from the analyses of the previous results and taking into account the typical user requirements, a preliminary design for the new algorithm based on Kalman filtering has been done. The idea underlying the new navigation system is to implement a simplified version of the so called adaptive Kalman filtering; the filter takes into account both the typical behaviour of an agricultural vehicle and the a priori knowledge of the planned track and works continuously testing alternate hypotheses in predicting the track. The new Kalman algorithm should both eliminate drifts in curves and occasional spikes in satellite configuration changes. This research project is still in progress; at the moment we have implemented the new algorithm which consists on a double filtering using the constant velocity model in straight trajectories and the constant acceleration model for curve tracks.

Problems during algorithm testing were mainly the lack of raw data, in fact TruRover NMEA reports are still filtered and there is not the possibility to remove the previous filter implemented inside the receiver and it is not mathematically correct utilizing them for another filtering. For

this reason data inputs for new algorithm have been provided from double frequency receiver without post-processing (raw data really as they have been stored). Results confirm the importance to adopt a model based on acceleration in curve, but at the same time it is necessary looking at these results in a critical way because they are outputs originated from inputs  better than Trurover data. In the tests the attention will be mainly focused on variables which have a great importance in the model and parameters choice, such as process covariance and measurement noise. Next steps will be two-fold:

− trying to vary covariance weighs both in system noise matrix and in measurement noise matrix;

− test double filtering with raw data not yet filtered and tracked by a low cost and single frequency receiver, showing located spikes.

The purpose to improve TruRover performances and to optimize them for precision farming is challenging, especially having at our disposal only raw data. Other possible solutions are:

− connecting an odometer and a steering wheel to the system, integrated with the GPS receiver, which supports human vehicle guide. It could be the input to choose, at the right time, the best process model to adopt inside Kalman filter (constant velocity or constant acceleration model).

− utilizing differential positioning, DGPS, improving coordinates thanks to corrections received from a Master station close to the field.

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GIS in Agriculture and Precision Farming

At present, GIS is being utilized by an extensive range of modern day industries; such as  logistics, criminology, disease control and many others. However, there is one particular field of use that is rapidly growing which is the field of agriculture. This is due to the fact that more and more farmers are realizing the value of GIS and how it can benefit them. Today there are  multiple usages of GIS in agriculture, the most prominent of which is precision farming.

Precision farming, or precision agriculture, is the farming concept that integrates geographical data obtained from technology such as GIS and GPS and helps optimize the yield  and lower the cost of agriculture (Goddard, Kryzanowski, Cannon, Izaurralde, & Martin, n.d.).

Furthermore, precision farming is not just beneficial to the farmers, it is also beneficial to nature  and the environment as it helps reduce the unnecessary impact of man and traditional farming  techniques on the environment (Goddard, Kryzanowski, Cannon, Izaurralde, & Martin, n.d.). With GIS and precision farming, farmers are able to determine the areas that need what in term  of nutrients, pest control and conditioning (“GIS and precision,” n.d.). This consequently reduces the need and cost of pesticides, fertilizers and other agro-chemicals because farmers can now  estimate the quantity of agro-chemical is actually required. This process is also known as “sitespecific” agriculture which refers to handling the smallest area of land as an independent element  (Pfister, 1998). Therefore, precision farming using GIS along with other geographical technology essentially improves farming overall by giving famers more specific information as to how to  treat or farm their crops in order to achieve maximum yield while reducing upkeep costs.

Precision farming consists of many different elements working together in synchronicity with the help of GIS. These multiple elements range from collecting and maintaining soil nutrient data before and during farming to yield monitoring during harvesting to see whether the optimized “site-specific” precision farming is effective (Pfister, 1998). The process for this farming technique is a year-round process that starts from before the planning process all the way to the harvesting process. After the harvest the famers still have to go through the data collected, analyze it and prepare a plan for the next round of crops.

The major parts of precision farming can mainly be broken up into three steps; preplanting, post-planting and during the growth, and lastly, during harvest (Pfister, 1998). The preplanting stage consists of gathering data required for planning. This includes, but is not limited to, gathering soil nutrient data from the farming area, gathering and evaluating data such as ground water level, potential pest and weed and also determining the areas, if any, affected by disease which may affect crop yields (Goddard, Kryzanowski, Cannon, Izaurralde, & Martin, n.d.). After planting and during the growth period, farmers have to use the data and information obtained during the pre-planting stage, through GIS, to manage different areas of the crops specifically depending on their needs. This may include dealing with weeds, diseases, pests and other problem affecting the crop (Pfister, 1998). The last stage is during harvesting, which the  famers use a technique called “yield monitor” which analyzes the data of the yield from the crow to see whether the overall plan is working and to prepare for the next farming period (Pfister, 1998). Data collected by the farmer is then fed into GIS software for analysis providing the farmer with the desired information and maps. It might even be said that GIS is the central component of precision farming. Without it, all the farmer would have is just a list of coordinates and random data. It is only after the data is analyzed using GIS software that it becomes useful information. Still, when looking at it from an overview perspective, the requirements and necessary steps in precision farming may seem daunting. Nonetheless, its effects and benefits seem to outweigh the necessary dedication on the part of the farmers which is clearly visible both from the level of adoption and the initial data being advertised.

Aside from yield monitoring, farmers rely on other related techniques. These include “variable planting,” “crop scouting,” “variable rate chemical application” .“Variable planting” is basically using the data  collected from previous crop cycles to help determine what should be planted where and how much of it should be planted. This can be automated by using seeding machines with yield monitoring data from previous crop seasons (Pfister, 1998). “Crop scouting” is the collection and track data on the growing crop, and the identification of any problems that may arise and the determination of what action should be taken to stop or eliminate the problem. “Variable rate chemical application” is related to automated sprayer systems in which the farmer can use the data previously collected for determining which areas, if any, need chemical intervention and if so how much is needed (Pfister, 1998). They do this by inserting the data into the machine responsible and it will take care of the rest (variable planting also uses similar methods). One other thing that farmers rely on for precision farming, that is not essentially a specific technique, is lab testing which is used for soil testing to determine information and data relating to “sitespecific” farming (Pfister, 1998) Right now, precision farming is being used by numerous countries around the world. In developing countries such as Malaysia, an adapted model of precision farming using GIS has and  is being tested . The adapted model that has been tested in Malaysia is for paddy-based  crops and grain such as rice (Aziz, Shariff, Soom, Rahim, Cha’Ya, & Jahanshiri, n.d.). What the  model shows is that there is no reason why precision farming using GIS cannot be applied to other types of crops, thus demonstrating that the concept of precision farming using GIS is  globally feasible. Even in the cold region of Alaska farmers are also using GIS-based precision farming (Brown, n.d.). What these models and research show is that, generally, GIS-based  precision farming is beneficial, if not right away then after the first few crop cycles. Basically, it is giving the farmer more knowledge using information technologies and systems to better manage his own crops. Whether it proves to be successful or not will rely largely on the farmer’s ability to manage his crops from the data he collected through the process and GIS programs. The benefits of precision farming using GIS are equally felt by all sectors related to it, from the farmers themselves to the consumers and even the environment itself. Aside from maximizing the yield while minimizing cost for farmers, precision farming using GIS is also beneficial in that it enables the farmer to make better decisions and to be more knowledgeable about their crops. It is beneficial to the consumer in that the yield product of the crops that have been grown using the precision farming is higher quality since farmers were able to tend to it specifically instead of treating the entire farmland as a whole (Pfister, 1998). It is beneficial to the environment because it reduces the amount of agro-chemicals needed for farming by identifying the areas that do need the chemical and at what level is sufficient and thus reducing the overall farming footprint (Nemenyi, Mesterhazi, Pecze, & Stepan, 2003).

However there are also some negatives and drawbacks regarding precision farming. For one, because it requires the farmer to invest in technologies that enable the ability to monitor their crops all before actually planting anything,  thus increasing the required farming cost in the short term (Lowenberg-DeBoer, 1996). Studies have also shown that long term profitability is not always achieved and some crops tend to be more profitable than others, mainly, high value crops such as vegetable and seeds as opposed to bulk crops such as corn (Lowenberg-DeBoer, 1996). Another drawback of precision farming is the fact that it requires investment and dedication on the part of the farmer right away but it usually takes time to effectively show up and achieve probability (Lowenberg-DeBoer, 1996). However, even with the information that is meant to help farmers make better, well thought-out decisions, the decision of how to treat their crops still ultimately falls to the farmer and if they lacks experience and decision-making skills or the how-to regarding the various tools and components that is needed, the outcome of it may be undesirable. Furthermore, even the slightest miscalculation may prove to be quite costly for the farmer. Lastly, let us not forget about the cost relating to the equipment and components used in precision farming. This cost depends on the usable lifetime of the equipment and components being used and may vary from average to moderate to expensive and costly (Lowenberg-DeBoer, 1996).

In summation, precision farming looks to be a promising agriculture concept that seems  to be overall beneficial to all involved; from the farmers themselves to the consumers like us. It looks to improve the efficiency of agriculture while also saving the environment. But ultimately, there are still some possible limitations of the concept. Due to the fact that it relies so much on the farmers and since the GIS software can only interpret the data that the farmer gives it, if the data that the farmer fed it is incorrect the resulting calculation will therefore be incorrect. In addition, while the concept helps improve the overall agricultural system, studies have shown that it is not an all-encompassing concept that is applicable to all. While some crops require some alteration for it to work, the method has proven to be ineffective for others, mainly bulk crops. So farmers should take this concept with a grain of salt, for the method may prove to be successful for some, it may also easily go the other way for others.

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What Is GIS?

A geographic information system (GIS) is a technological tool for comprehending geography  and making intelligent decisions.

GIS organizes geographic data in such way that a person reading a map can select data needful for a certain project or task. A thematic map has a table of contents that provides opportunity to the reader to add layers of information to a basemap of real-world locations. For instance, a social analyst might use the basemap of Eugene, Oregon, and select datasets from the U.S. Census Bureau to add data layers to a map that shows residents’ education levels, ages, and employment status. With an ability to combine a variety of datasets in an infinite number of ways, GIS is a useful tool for nearly every field of studying.

A good GIS program is able to process geographic data from a variety of sources and integrate  it into a map project. Many countries have an abundance of geographic data for analysis, and  governments often make GIS datasets publicly available. Map file databases often come included  with GIS packages; others can be obtained from both commercial vendors and government agencies. Some data is gathered in the field by global positioning units that attach a location  coordinate (latitude and longitude) to a feature such as a pump station.

GIS maps are interactive. On the computer screen, users can scan a GIS map in any direction,  zoom in or out, and change the nature of the information contained in the map. They can choose  whether to see the roads, how many roads to see, and how roads should be depicted. Then  they can select what other items they wish to view alongside these roads such as storm drains,  gas lines, rare plants, or hospitals. Some GIS programs are designed to perform sophisticated  calculations for tracking storms or predicting erosion patterns. GIS applications can be embedded  into common activities such as verifying an address.

From routinely performing work-related tasks to scientifically exploring the complexities of our world,  GIS gives people the geographic advantage to become more productive, more aware, and more responsive citizens of planet Earth.

The advantages of GIS using in agricultural include the following:

  • Work efficiency
  • Revenue generation and cost recovery
  • Improved accuracy
  • Task automation
  • Time and cost savings
  • Decision-making support
  • Resource management

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Agriculture crop management and production was improved by satellite remote sensing technology and geographic information systems (GIS)

Scientists for many years have been using satellite remote sensing technology, utilizing low and medium resolution sensors to improve water balance and farming yield assessment on large geographical scales around the world.

With the availability of high resolution satellite sensors such as IKONOS, QuickBird and soon GeoEye-1, the current remote sensing NDVI algorithms utilized have become more accurate and reliable, providing detailed crop information for agriculture management to improve production and crop health.

FAO (Food and Agriculture Organization of the United Nations) data indicate that annually 2500 km3 of freshwater is used for agricultural production, which amounts to 70% of the water resources that the world population consumes in a year. China is now consuming more than twice as much as what its ecosystems can supply sustainably, having doubled its needs since the 1960s, as indicated in a new WWF report. With the global population continuing to grow at a high pace, it is essential to optimise the use of water resources and to increase agricultural production in view of the prospect of having to feed 8 billion humans by 2030.

Agriculture resources are among the most important renewable, dynamic natural resources. Comprehensive, reliable and timely information on agricultural resources is very much necessary for countries whose main source of the economy is agriculture. Agriculture surveys are conducted through the nation in order to gather information and statistics on crops, rangeland, livestock and other related agricultural resources. This data is most important for the implementation of effective management decisions.

Satellite images can show variations in organic matter and drainage patterns. Soils higher in organic matter can be differentiated from lighter sandier soil that has a lower organic matter content. “Satellite image data have the potential to provide real-time analysis for large areas of attributes of a growing crop that can assist in making timely management decisions that affect the outcome of the current crop” said Leopold J. Romeijn, President of Satellite Imaging Corporation of Houston, Texas. However, like other precision agriculture technologies the information gained from satellite imagery are more meaningful when used with other available data and visualised and analysed with a 2D/3D Geographical Information Systems (GIS).

Satellite Imagery analysis for agriculture production allows for: fast and accurate overview; quantitative green vegetation assessment; underlying soil characteristics; treeGrading.

Remote sensing satellite imaging is an evolving technology with the potential for contributing to studies for land cover and change detection by making globally comprehensive evaluations of many environmental and human actions possible. These changes, in turn, influence management and policy decision-making. Satellite image data enable direct observation of the land surface at repetitive intervals and therefore allow mapping of the extent and monitoring and assessment of: crop health; storm water runoff; change detection; air quality; environmental analysis; energy savings; irrigated landscape mapping; carbon storage and avoidance; yield determination; soils and fertility analysis.

Normalized Difference Vegetation Index (NDVI)

The Normalized Difference Vegetation Index (NDVI) is a simple numerical indicator that can be used to analyse remote sensing measurements from a space or airborne platform, and assess whether the target being observed contains live green vegetation or not.

High or medium resolution satellite image data products help quantify crop status, soil conditions and rates of crop change throughout the field as small as 2’ x 2’. NDVI products reduce the field time by 50% by quickly identifying the problem areas – often before they are visible to the naked eye and to provide a solution to the problem which can significantly boost field productivity and crop quality, while reducing costs.

Green Vegetation Index – Colorized Map

The Green Vegetation Index – Colorized Map (GVC) colourises the green vegetation index (GVI) values to show the spatial distribution of remotely sensed vegetation. The index is related to crop vigour, vegetation amount or biomass, resulting from inputs, environmental, physical and cultural factors affecting crops. The NDVI algorithm is applied to calibrate satellite images to separate the reflectance of vegetation from variation caused by underlying soils or water. The product is produced for a given field as well as for a region of interest.

Green Vegetation Index – Sharpened Map (GVS) is a superior product which combines pansharpened information and GVC values to improve manual image interpretation intended to facilitate the identification and mapping of significant spatial features. Information about green biomass density is contained in the original GVC product, which uses colours to show various levels in increments of 5 (on a scale from 0 to 100). GVS uses the registered panchromatic image (collected to make this a visible pan image) to modulate the brightness of each GVC colour. The result has the excellent properties of a pansharpened image, but with quantitative numbers that are close (within 2 units) of the original GVC numbers.

Soil Zone Index

To develop a Soil Zone Index map, satellite images of the agriculture fields are calibrated and then spectral algorithms are applied that isolate soil components from vegetation. The final satellite image shows what the soil surface of your field looks like, including irrigation patterns, sand streaks, clay lenses, and organic matter and crop residue variations. If the crop has less than 50 percent canopy cover, the NDVI algorithms sees it all, and the Soil Zone map shows only the underlying soil. With a Soil Zone map, you can clearly see landscape variations. Lighter colours indicate dry, salty or coarsely textured soils, while darker colours indicate wet or organic soils. Often, variations in colour indicate topographic variations across fields, which can greatly impact your crop management strategies and zone creation for precision agriculture management applications.


The TreeGrading product provides an assessment of each individual tree in an orchard to help growers manage trees for top production. High resolution QuickBird, IKONOS or SPOT-5 satellite image data can be collected in support of Agriculture Management developing TreeGrading Maps to reveal the location and extent of each tree canopy determined by using a proprietary spectral algorithm. The properties of the GVI satellite images within each polygon are extracted to an industry standard Geographic Information Systems (GIS) database. The GIS software is then used to view and analyse the data. A satellite image and GIS map of missing trees is also created so the manager can plan for replacement.

Satellite Imaging Corporation (SIC) provides archived and new IKONOS, QuickBird, SPOT-5, ALOS and other Satellite Image data for many areas around the Globe and utilizes advanced Remote Sensing techniques, Colour and Panchromatic image data processing services, orthorectification, culture and feature extractions, pan sharpening with image data fusion from different sensors and resolutions, enhancements, georeferencing, mosaicing and colour/grayscale balancing for GIS and other geospatial applications.

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