Crop Monitoring & Damage Assessment

Crop health evaluation, as well as early detection of crop infestations, is critical in ensuring high agricultural productivity. Stress associated with, for example, moisture deficiencies, insects, fungal and weed infestations, must be detected early enough to provide an opportunity for the farmer to mitigate. This process requires that remote sensing imagery be provided on a frequent basis (at a minimum, weekly) and be delivered to the farmer quickly, usually within 2 days.

Also, crops do not generally grow evenly across the field and consequently crop yield can vary greatly from one spot in the field to another. These growth differences may be a result of soil nutrient deficiencies or other forms of stress. Remote sensing allows the farmer to identify areas within a field which are experiencing difficulties, so that he can apply, for instance, the correct type and amount of fertilizer, pesticide or herbicide. Using this approach, the farmer not only improves the productivity from his land, but also reduces his farm input costs and minimizes environmental impacts.

There are many people involved in the trading, pricing, and selling of crops that never actually set foot in a field. They need data regarding crop health worldwide to set prices and to negotiate trade agreements. Many of these people rely on products such as a crop assessment index to compare growth rates and productivity between years and to see how well each country’s agricultural industry is producing. This type of information can also help target locations of future problems, for instance the famine in Ethiopia in the late 1980’s, caused by a significant drought which destroyed many crops. Identifying such areas facilitates in planning and directing humanitarian aid and relief efforts.

Remote sensing has a number of attributes that lend themselves to monitoring the health of crops. One advantage of optical (VIR) sensing is that it can see beyond the visible wavelengths into the infrared, where wavelengths are highly sensitive to crop vigour as well as crop stress and crop damage. Remote sensing imagery also gives the required spatial overview of the land. Recent advances in communication and technology allow a farmer to observe images of his fields and make timely decisions about managing the crops. Remote sensing can aid in identifying crops affected by conditions that are too dry or wet, affected by insect, weed or fungal infestations or weather related damage. Images can be obtained throughout the growing season to not only detect problems, but also to monitor the success of the treatment.

Healthy vegetation contains large quantities of chlorophyll, the substance that gives most vegetation its distinctive green colour. In referring to healthy crops, reflectance in the blue and red parts of the spectrum is low since chlorophyll absorbs this energy. In contrast, reflectance in the green and near-infrared spectral regions is high. Stressed or damaged crops experience a decrease in chlorophyll content and changes to the internal leaf structure. The reduction in chlorophyll content results in a decrease in reflectance in the green region and internal leaf damage results in a decrease in near-infrared reflectance. These reductions in green and infrared reflectance provide early detection of crop stress. Examining the ratio of reflected infrared to red wavelengths is an excellent measure of vegetation health.  This is the premise behind some vegetation indices, such as the normalized differential vegetation index (NDVI). Healthy plants have a high NDVI value because of their high reflectance of infrared light, and relatively low reflectance of red light. Phenology and vigour are the main factors in affecting NDVI. An excellent example is the difference between irrigated crops and non-irrigated land. The irrigated crops appear bright green in a real-colour simulated image. The darker areas are dry rangeland with minimal vegetation. In a CIR (colour infrared simulated) image, where infrared reflectance is displayed in red, the healthy vegetation appears bright red, while the rangeland remains quite low in reflectance.

Examining variations in crop growth within one field is possible. Areas of consistently healthy and vigorous crop would appear uniformly bright. Stressed vegetation would appear dark amongst the brighter, healthier crop areas. If the data is georeferenced, and if the farmer has a GPS (global position satellite) unit, he can find the exact area of the problem very quickly, by matching the coordinates of his location to that on the image.

Detecting damage and monitoring crop health requires high-resolution imagery and multispectral imaging capabilities. One of the most critical factors in making imagery useful to farmers is a quick turnaround time from data acquisition to distribution of crop information. Receiving an image that reflects crop conditions of two weeks earlier does not help real time management nor damage mitigation. Images are also required at specific times during the growing season, and on a frequent basis.

Remote sensing doesn’t replace the field work performed by farmers to monitor their fields, but it does direct them to the areas in need of immediate attention.

Canada vs. International

Efficient agricultural practices are a global concern, and other countries share many of the same requirements as Canada in terms of monitoring crop health by means of remote sensing. In many cases however, the scale of interest is smaller – smaller fields in Europe and Asia dictate higher resolution systems and smaller areal coverage. Canada, the USA, and Russia, amongst others, have more expansive areas devoted to agriculture, and have developed, or are in the process of developing crop information systems (see below). In this situation, regional coverage and lower resolution data (say: 1km) can be used. The lower resolution facilitates computer efficiency by minimizing storage space, processing efforts and memory requirements.

As an example of an international crop monitoring application, date palms are the prospective subject of an investigation to determine if remote sensing methods can detect damage from the red palm weevil in the Middle East. In the Arabian Peninsula, dates are extremely popular and date crops are one of the region’s most important agricultural products. Infestation by the weevil could quickly devastate the palm crops and swallow a commodity worth hundreds of millions of dollars. Remote sensing techniques will be used to examine the health of the date crops through spectral analysis of the vegetation. Infested areas appear yellow to the naked eye, and will show a smaller near infrared reflectance and a higher red reflectance on the remotely sensed image data than the healthy crop areas. Authorities are hoping to identify areas of infestation and provide measures to eradicate the weevil and save the remaining healthy crops.

 (Source – http://www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/fundamentals/1696)

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Potential monitoring of crop production using a satellite-based Climate-Variability Impact Index

Crop monitoring and early yield assessment are important for agriculture planning and policy making atregional and national scales. Numerous crop growth simulation models are generated using crop state variables and climate variables at the crop/soil/atmosphere interfaces to get the pre-harvest information on crop yields. However, most of these models are limited to specific regions/periods due to significant spatial–temporal variations of those variables. Furthermore, the limited network of stations and incomplete climate data make crop monitoring and yield assessment a daunting task. In addition, the meteorological data may miss important variability in vegetation production, which highlights the need for quantification of vegetation changes directly when monitoring climate impacts upon vegetation. In this sense, remotely sensed metrics of vegetation activity have the following advantages: a unique vantage point, synoptic view, cost effectiveness, and a regular, repetitive view of nearly the entire Earth’s surface, thereby making them potentially better suited for crop monitoring and yield estimation than conventional weather data. For instance, it has been shown that the application of remotely sensed data can provide more accurate crop acreage estimates at national/continental scales. Furthermore, numerous field measurements and theoretical studies have demonstrated the utility of remotely sensed data in studies on crop growth and production. These two applications suggest the feasibility of large-scale operational crop monitoring and yield estimation.

Empirical relationships between the remotely sensed data and crop production estimates have been developed for monitoring and forecasting purposes since the early 1980s. For instance, Colwell found a strong correlation between winter wheat grain yield and Landsat spectral data. However, these relationships did not hold when extended in space and time (Barnett and Thompson, 1983). Later, various other vegetation indices generated from Landsat data, such as the ratio of the reflectance at near infrared to red and the normalized difference vegetation index (NDVI) were used in yield estimation of sugarcane, wheat, and rice. The Landsat series have a spatial resolution of 30 m and can provide reflectance data from different spectral bands. However, these highresolution data require enormous processing effort, and  may not be applicable for surveys of large-area general crop conditions.

Vegetation indices derived from data from the Advanced Very High Resolution Radiometer (AVHRR) were also used for crop prediction, environmental monitoring, and drought monitoring/assessment. For example, found that millet yields in northern Burkina Faso are linearly correlated with the AVHRR NDVI integrated over the reproductive period. Similarly, Hochheim and Barber found that the accumulated AVHRR NDVI provided the most consistent estimates of spring wheat yield in western Canada. The Vegetation Condition Index (VCI) derived from AVHRR data is widely applied in real-time drought monitoring and is shown to provide quantitative estimation of drought density, duration, and effect on vegetation. The VCI can separate the short-term weather signals in the NDVI data from the long-term ecological signals. According to Domenikiotis , the empirical relationship between VCI and cotton yield in Greece are sensitive to crop condition well before the harvest and provide an indication of the final yield. Unfortunately, the AVHRR data are not ideally suited for vegetation monitoring.

Data

1. The MODIS land-cover classification product identifies 17 classes of land cover in the International Geosphere–Biosphere Programme (IGBP) global vegetation classification scheme. This scheme includes 11 classes of natural vegetation, 3 classes of developed land, permanent snow or ice, barren or sparsely vegetated land, and water. The latest version of the IGBP land-cover map is used to distinguish croplands from the other biomes in this research.

2. MODIS LAI

The retrieval technique of the MODIS LAI algorithm is as follows. For each land pixel, given red and near infrared reflectance values, along with the sun and sensor-view angles and a biome-type designation, the algorithm uses model-generated look-up tables to identify likely LAI values corresponding to the input parameters. This radioactive transfer-based look-up is done for a suite of canopy structures and soil patterns that represent a range of expected natural conditions for the given biome type. The mean value of the LAI values found within this uncertainty range is taken as the final LAI retrieval value. In certain situations, if the algorithm fails to localize a solution either because of biome misclassification/mixtures, high uncertainties in input reflectance data or algorithm limitations, a backup algorithm is utilized to produce LAI values based upon the empirical relationship between NDVI and LAI (Myneni et al., 1997).

The latest version of MODIS global LAI from February 2000 to December 2004 was taken to characterize the crop activity in this study. The 8-day LAI products are distributed to the public from the Earth Observing System (EOS) Data Gateway Distributed Active Archive Center. The 8-day products also provide quality control variables for each LAI value that indicate its reliability. The monthly global product was composited across the 8-day products using only the LAI values with reliable quality. The monthly global products at 1-km resolution with Sinusoidal (SIN) projection are available at Boston University. In this paper, monthly LAI at 1-km resolution are used to generate our Climate-Variability Impact Index. As these will be compared with estimates of crop production reported at county/state-levels, the vegetation-based CVII fields were aggregated over the corresponding counties/states using the county bound arias 2001 map from the National Atlas of the United States.

3. AVHRR LAI

AVHRR LAI is used as a substitute for the MODIS LAI to examine the temporal characteristics of vegetation activity over longer time periods. The AVHRR LAI is derived from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI produced by NASA GIMMS group. Monthly LAI from 1981 to 2002 at 0.258 were derived based on the empirical relationship between NDVI and LAI for different biomes. Literature works show that this empirical relationship might be different for the same biome at different locations. To eliminate this effect, models are generated for each pixel to calculate GIMMS LAI from GIMMS NDVI. The MODIS LAI and GIMMS NDVI overlapped from March 2000 to December 2002, which provides a basis for generating a piecewise linear relationship between these two products. Once the coefficients of the linear model are calculated, the whole range of GIMMS NDVI can be converted into GIMMS LAI, which is consistent with the MODIS products. Our preliminary results indicate a good agreement between GIMMS LAI and MODIS LAI at quarter degree resolution with less than 5% relative difference for each main biome (results not shown).

4. GIMMS NPP

In this research, we also use model-generated estimates of Net Primary Production (NPP) from Nemani  as a predictor of crop production. This NPP is a monthly product from 1982 to 1999 at a spatial resolution of half degree. This global NPP product was generated as follows. GIMMS NDVI were first used to create LAI and FPAR with a 3D radiative transfer model and a land-cover map as described in Myneni. Then, NPP was estimated from a production efficiency model (PEM) using the following three components: the satellite-derived vegetation properties, daily climate data, and a biome specific look-up table of various model constants and variables. Further details can be found in Nemani et al. (2003).

5. Crop production

Crop production data from several sources are used in this research. We focus upon total production, as opposed to yield, for instance, because although the two are highly correlated with each other, total production is typically the parameter of interest for crop monitoring and yield prediction. In this paper, we will explicitly refer to ‘‘production’’ when discussing quantitative results, however for simple qualitative statements wesometimes retain the generic term ‘‘yield’’ as synonymous for ‘‘production’’. The country-level crop production from 1982 to 2000 in European countries is from FAOSTAT 2004 data set. The county-, district-, and state-level production data in United States are from the National Agricultural Statistics Service (NASS) at United States

Department of Agriculture (USDA) USDA provides two independent sets of county crop data: one is a census of agriculture, which is released every 5 years; the other one is annual county crop data, which is based on reports from samples. We used the annual crop estimates in this study. Due to the processing effort required for the fine resolution remotely sensed data, we studied two crops (corn and spring wheat) in two US states (Illinois and North Dakota) at county- and district-scales. At coarser scales, we expanded the regions to include Illinois (IL), Minnesota (MN), Michigan (MI), Iowa (IA), Indiana (IN), and Wisconsin (WI) for corn; to North Dakota (ND), Montana (MT), Minnesota (MN), and South Dakota (SD) for spring wheat; to Kansas (KS), Oklahoma (OK), Colorado (CO), and Nebraska (NE) for winter wheat. The county- and district-level data of Illinois and North Dakota are from 2000 to 2004; the state-level data are from 1982 to 1999.

(Source –  http://cybele.bu.edu/download/manuscripts/zhping02.pdf)

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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.

TreeGrading

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.

(Source – http://eomag.eu/articles/671/agriculture-crop-management-and-production-improved-by-satellite-remote-sensing-technology-and-geographic-information-systems-gis)

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Satellite Based Crop Monitoring System in Pakistan

Pakistan is a country of diverse agro-climatic regions. The climate is  predominantly arid to semi arid. The mighty Indus and its tributaries have  facilitated the establishment of a network of dams, barrages and a profuse delivery system of water supplies. Pakistan’s agriculture is predominantly converged in the Indus basin.

In 2005 erstwhile Ministry of Food and Agriculture (MINFA) opted to  invest in advanced technologies for  gathering spatial information on  agriculture/ crops sector. For this purpose, MINFA invited SUPARCO,  the National Space Agency of  Pakistan, to develop crop area  algorithms and crop yield models,  based on the application of satellite remote sensing, GIS technology, crop  agronomy and agro-meteorology.

Worldwide Satellite based crop Monitoring Systems A number of countries and organizations, worldwide are currently involved in  monitoring crops, using satellite technology and allied systems. The most  important of these include Food and Agriculture organization of the United  Nations, European Union, USA, China, and a number of others. Description of  these programs is as follows.

FAO: Global Information and Early warning System (GIEWS), GIEWS provides up to dated information on the food security situation of  developing countries. It furnishes country specific information on current

agricultural season and the harvest prospects for main staple food crops and  livestock situation. In addition, the system provides estimates and forecasts of cereal production and imports together with food price and policy  developments. The briefs are updated no less than four times per year.

MARS, European Union (EU) The EU is running a program titled Monitoring Agriculture Resource System

(MARS) at Joint Research Center (JRC), Milan Italy. The basic purpose of this  program is to provide timely information pertaining to crop yield forecasting  system  USA: Crop Explorer; Foreign Agricultural Service (FAS), USDA The Crop Explorer web portal features near-real-time global crop condition  information based on satellite imagery and weather data. Thematic maps of  major crop growing regions are updated every 10 days to depict the latest  statistics pertaining to vegetative vigor, precipitation and temperature, and  soil moisture. Time-series charts depict current and historical growing season

data for specific agro-meteorological zones.

China Crop Watch System (CCWS) The China Crop Watch System (CCWS) was developed by the Institute of  Remote Sensing Application (IRSA) of the Chinese Academy of Sciences (CAS)  in 1998. CCWS covers entire China and 46 major grain-growing countries of  the world. The System monitors the condition of the growing crop, crop  production, drought, crop plantation structure and cropping index.

Pakistan: Satellite based Crop Monitoring System (Pak-SCMS) SUPARCO in collaboration with erstwhile MINFA, started developing a  satellite based crop Monitoring system during 2005 to provide fast track and  accurate information on crops and also cover any catastrophic situations.

Agricultural mask of Pakistan was developed based on high resolution data  acquired during peak growth seasons of February for Rabi crops and  September for Kharif crops. SUPARCO carries out wall to wall coverage of the  agriculture area of the country using remote sensing data. This data is utilized to monitor various crops across the seasons. SUPARCO also has developed a  regional crop calendars for sowing and harvesting of crops to be used for  acquisition of satellite data during Rabi and Kharif seasons. Field surveys are also organized to collect spectral signatures of crops and land surface

features.

In addition to satellite imaging program, SUPARCO has developed an area  frame system for Pakistan, based on satellite image acquisition. This was done through Stratification of land-cover area. The decadal NDVI (Normalized  Difference Vegetation Index) was used to stratify the land-cover features at

maximum peak vegetation stages in the last decade of February for Rabi crops  and second decade of September for kharif crops.

The crop yield models are based on the concept of harmonization and integration of historical data of crops, weather systems, fertilizers and  satellite vegetation information, with corresponding data of these variables  during the year under study.

Now that MINFA has been devolved and the subject is being handled by the  provincial Governments, SUPARCO continuous to interact with all the  concerned departments in the provinces ensuring extending remote sensing & GIS tools for better agriculture planning & monitoring in Pakistan.

Conclusion

The satellite remote sensing and GIS technology has helped to overcome the  limitations of manual system. This technique has been useful to supply temporal and synoptic data of high quality in advance of crop harvests. This  has also helped to monitor natural calamities as floods and drought. A  monthly web based crop forecasting service(http://www.suparco.gov.pk/pages/pak.scms.asp) has been started to  provide country wide authenticated and scientific information on crops. The  planners, policy makers, public, private sector and other end users have greatly benefitted from this service).

(Source – http://www.suparco.gov.pk/downloadables/12-Satellite-based-Montoring-of-Agriculture.pdf)

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Satellites as a bridge to new agronomic era

Nowadays it is hard to impress somebody with satellite launching. Though just 60 years ago it was like a fantastic tale. Nobody thought that it can be possible to see the photos of your house, street or field made from the space. In this article the issue of modern achievement, which became available thanks to satellite systems  and their influence on agrarian business will be discussed.

Achievement 1. Navigation.

Due to satellites the system of navigation GPS, which is now used for determination of  location and direction on air (aircrafts), on water (ships), and on land became possible. An advantage of this system is that it provides opportunity for any place (excluding polar region), almost in all weather conditions, to indicate the speed and  objects location. The basic principle here – the determination of the location by measuring the reception time of the synchronized signal from satellite to the consumer.

Achievement 2. Weather and climate control.

Satellites give possibility to explore the weather around the world, allowing them to follow the effects of phenomena like volcanic eruptions and burning gas and oil fields.

Satellites are the best sources of data for climate changes research. Satellites monitor ocean temperatures and prevailing currents; rise/drop of the sea levels, the changing sizes of glaciers. Satellites can determine long-term patterns of rainfall, vegetation cover, and emissions of greenhouse gases.

Achievement 3. Land Stewardship

Satellites can detect underground water and mineral sources; monitor the transfer of nutrients and contaminants from land into waterways, and the erosion of topsoil from land. They can efficiently monitor large-scale infrastructure, for example fuel pipelines that need to be checked for leaks.

As we can see, satellites have changed both: our leisure time and business, provoked the emergence of new agricultural technologies. We got possibility of more accurate prediction of changes in climate and weather, which is very important for farmers. Satellites have made possible simplification and improvement of the process of soil nitrogen saturation. We would like to highlight the following:

1. GPS+GIS

You can equip the tractor with signal receiver GPS, heading sensor and controller – the screen that reflects the identity or deviation from the path of the tractor predetermined. The control system allows you to store and forward rate tractor strictly parallel to the line that is fixed on the first pass of the unit, the second option – autopilot, which consists of electro-hydraulic automatic control of the tractor, which provides tractor autopilot on the field. Tractor-driver helps the process only while cornering, allowing it to focus on the process and less physically tired.

GIS (Geographic information system) – the system of collection, storage, analysis and graphical visualization of spatial (geographical) data and related information on the necessary facilities.

A new and promising directions in agriculture abroad is precision agriculture. The concern is that to use the heterogeneous data (the geographically-referenced results of soil sampling, remote sensing data processing, digital thematic maps) to optimize decision-making on the local application of fertilizers and pesticides into the soil to boost agricultural productivity.

2. Satellite crop monitoring

Technology based on spectral analysis of high resolution satellite crop images which enables to monitor vegetation developments, soil temperature,  humidity and to reveal problem areas on the field. Satellite crop monitoring is also suitable to precise weather forecast based on concrete field coordinates and to recall historical weather data retrospection. Discrepancy in NDVI dynamics reports about the disparities in development within a corn or a field that indicates the need for additional agricultural activities in some areas.

In conclusion, we can say that due to modern technologies as satellites we construct our future, and in order to go with the times it is very important to know about them and to use them, because combination of them with your experience make your business more efficient and with less time and effort costly.

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Vegetation Control: Profitable Investment

Agricultural complex has always been one of the most significant propelling forces of the Ukrainian economy. Agriculture provides for 8% of total GDP, covering about 71% of the territory and 17% of working population employment. Meanwhile, in comparison with their foreign peers, the performance indicators of domestic agrarian companies demonstrate disproportion in operating results. Thus, if we scrutinize developing countries with agricultural specialization, Ukraine will demonstrate the highest rate of territory used for crop production (0.71 hectares per citizen) with a relatively small contribution to the GDP (-3−8% to the other countries level).

This correlation is reflected in the grain crop yield, which ranks Ukraine far behind the majority of leading grain-producing countries. Extensive management methods have led to the degradation of the black soil and thus – to the increase in expenses required per unit of cultivated area, fertilizers in particular.

It is impossible to improve  productivity performance in Ukraine without a substantial growth in mineral fertilizers use. However, global (reduction of the discrepancy between demand and production capacity) and domestic (deficit of phosphate fertilizers, potash fertilizer production standstill, increasing natural gas price) factors lead to constant rise in fertilizers prices for Ukrainian farmers and as a result lead to the profitable investments.

In addition, it should be noted that prices for agricultural machinery, fuel and pesticides have significantly increased. Even despite the fact that crop prices in 2011 have risen on average by 15%, the increase in prices for machinery and other accompanying expenses was more substantial.

The human capital costs have changed in a similar manner: the average nominal wage of an agricultural worker has increased by 25.9%during the last year. Still the Ukrainian agriculture industry employs +5-10% more population as compared to the European countries, while every employee produces 2-5x times less quantity of the added value.

In 2011 the expenses for fertilizers, POL and wages constituted almost 40% of crop production cost, thus during only one year the price jump caused spending spree by 15% and even more for specific crops. Let alone the cost of spare parts and materials for the repair of machinery and buildings (+0.3% in the total cost structure) and the rising costs for seed grain. In other words, such a scenario allows to catch up with the price increase tendency, but arouses the necessity to renovate obsolete production capacities, agriculture machines fleet, which also become less affordable, especially in terms of “price-quality” ratio. For example, some items from leading international equipment producers which are available in Ukraine have raised in price by 10% and even more in comparison with the analogue machines from the CIS.

Thus the problem is to find internal sources of production cost reduction to compensate the cost increase, which is beyond the agricultural holding control (fuel and metal prices, fertilizers deficiency, etc.). Global agricultural products prices do not depend on production cost in Ukraine, so it is the agrarian’s business to reduce it by cultivation cost-cutting. For this reason, developed countries opt for the use of precision agriculture system based on computer analysis of remote crops sensing data (RSD). Some Ukrainian farms already use such agricultural technologies as geologic information systems (GIS) and global positioning system (GPS). But in such a limited format these technologies are rather used to control equipment fleet maintenance, fuel input rationality and adequate farm maps creation. In the course of our cooperation with the agricultural companies management, we have discovered that a maximum allowable innovation is considered to be the purchase of expensive foreign equipment, its GPS monitoring installation and the creation of interactive maps of soils of rather satisfying quality. But precision agriculture implies exactly the efficient usage of every single asset. Even a large fleet of tractors can not effectively cultivate the fields without additional instructions on problem areas, non-rational heavy fertilization can be harmful and interactive maps do not allow to understand the current field condition in a real-time mode. It proves to be a real problem for the large farms as they simply fail to control the vegetation on their fields, and thus to identify in time the causes of low crop yield in different regions.

The main condition for the high-efficient GPS and GIS deployment is close cooperation with the system of constant remote vegetation control of field crops. Altogether, this forms the organizational strategic units . This scheme enables to attract fewer workers to control vegetation, field works planning and maintenance of communication between individual units and subunits of agro-enterprises. The vegetation control system performs constant monitoring of agro-enterprises soils irrespective of the distance among the fields and of the crops planted. Upon the abnormal “spot” appearance on the field, the person in charge receives a message and the agronomist makes appropriate decisions regarding fertilization, irrigation or other cultivation arrangements. We have to admit that other methods of soil monitoring (driving around the fields, installation of special observing equipment on certain areas, taking soil pieces for laboratory analysis, etc.) are less informative but consume more time and funds. In addition, each of the observations is far more difficult to organize and to hold than to download all required current and historical data (with its automatic interpretation) from any computer connected to the Internet.

As the conducted research have revealed, the cost of the each service on average starts from $1.5 a year per hectare, depending on the total farm area the system maintains. At the same time, this service allows to save $3-5/ha and to make profit from efficiency performance increment (e.g. for winter wheat) starting from $13/ha. In other words, every invested dollar gives an opportunity to earn 18 times more by reducing costs and increasing the efficiency of crops cultivation. For instance, if 10 largest domestic agricultural holdings have a land domain of more than 150,000 ha each, the application of RSD satellite analysis will bring a profit measured in tens of millions US dollars.

Nowadays, only a few domestic companies in Ukraine render services of crop vegetation control. However this derives from a low demand due to conservatism of the most agrarians, their general aversion to high technologies and short period of presence of such services in the domestic market. On the other hand, internal trends in the agricultural sector indicate that the next steps of the businessmen in agricultural field will become optimization of assets and search for sources to improve their operation efficiency.

Tags: satellite, precise agriculture, GIS, GPS, crop, Ukraine, efficiency, wheat, sunflower, barley, legumes

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Precision Agriculture and the Ukrainian Reality

The Committee on World Food Security research shows that nowadays global welfare largely depends on the dynamics of food production. We have learned how to synthesize scarce sapphire crystals and how to replace expensive petrol with biofuel, but still unable to cope with the hunger problem. The world population is growing, while the area of free land for the expansion of crops is limited – another deforestation or swamps draining is a potential threat of ecological disaster. Profits from sunflower oil, wheat or sugar sales in the world market are comparable to machinery and coal trade profitability. The question is why more than a third of Ukrainian agricultural enterprises are unprofitable in such favorable environment? What methods of doing business help global leaders of the agricultural market work more efficiently than domestic companies that have rich Ukrainian soils?

Ukrainian-type Efficiency of Agricultural Business

The necessity to pursue the way of the intensive agricultural development became evident to the most developed countries long ago. The most recent developments in science and technology are applied not only to space rocket engineering, but to work in the field as well. Modern agricultural machinery is equipped with computers, new varieties of crops are grown in the laboratories, whilst satellites and drones are watching crops of large landowners in real-time. Nowadays agriculture of developed countries turns to an absolutely new level of competition – the efficient one. In a market where you can not control the price, you must manage the prime cost or go away. Agricultural market has become so global that the most effective way to manage profitability is to manage production costs. Modern wars are held without tanks and infantry – just one rocket is enough if it hits the enemy’s camp navigated from space and powered with minimum resources, but reaching maximum effect. The same processes take place in the agrarian sector. All the efforts have been turned to allocate available resources with the highest efficiency to achieve the utmost result.

The general picture of precision agriculture in Ukraine calls for new efficient reforms at least to overtake the leading world agrarian producers. Let us turn to statistics. According to the State Statistics Service of Ukraine, since the proclamation of independence in Ukraine the level of the plowed area reached nearly 72% of total country territory which is one of the highest indices value in the world. At the same time the production volume of cereals/legumes and sugar beets per capita declined in comparison with 1990 by 13% and 65% respectively. This means that the extensity of using acreage has not justified itself. The same is about the excessive use of another available resource in Ukraine – manpower. In this country over 16% of the population is employed in the agricultural sector (according to the FAO research, this figure does not exceed 9% for the developed countries), but the number of added value created by one employee is “only” 2,500 dollars per year (in the U.S. it equals to 51,000 dollars, in Romania – 9,700, in Poland – 3,000).

Significant gap between leading countries and Ukraine can be actually closed only with the help of many millions of investments that seems very difficult within the global financial crisis. The lack of “long-term money” (the payback period in the agriculture reaches at least 5-7 years even in the most optimistic scenario) multiplied by the lack of investments defend guarantees, inflation and instability of commodity markets, creates difficulties to obtain financing even for the largest agricultural holdings. However, it is hardly possible to overtake the world leaders by the other way in the current business environment: according to the World Bank, Ukrainian reality shows us that Ukraine has one of the lowest rates of fertilizer expenses and tractor use per unit of cultivated area, the average productivity depending on the crop is lower than world analogs in two or three times, each planting and harvesting campaign has deficiency of oil products whose production in Ukraine is insufficient because of a lack of raw materials and general equipment deterioration.

Let’s Change the Principles!

In such a situation the Ukrainian agricultural sector needs to find alternative ways for the further development. It’s half of the problem when we lose our export positions – much worse is when we have to purchase the agricultural production for foreign currency abroad like it regularly happens with sugar. If oil, natural gas, phosphates and other raw materials for agriculture are rising in price, it is better to optimize their consumption per unit of cultivated area to achieve maximum efficiency in each field, for each type of crops. The one who will be able to offer worthy quality at a reasonable market price, without doing himself out of his share and his interest as an entrepreneur, can be a winner in the market competition. It is necessary to abandon an unprofitable principle of explicit loss and to switch to smart management “as needed”. What’s the use of distributing fertilizers evenly, if only few fields or areas within the field need more fertilizers, while the others have the surplus? Does it make sense to go around the crops every day to check whether everything is in order, if there are systems for identifying problem fields? Why do we use the weather forecast for the nearest settlement, if we need the weather for a particular field in two dozen kilometers away? These are the questions which have become the philosophy of agribusiness in developed countries but are not that popular among the Ukrainians.

Precision Agriculture

It is possible to achieve the result described above with the help of so-called “precision agriculture” – the use of the concept about the existence of heterogeneity within a single field or planting. Such features could be caused by the landscape specifics, soil composition and proximity of mineral layers, condition of groundwater, climatic characteristics and features of crops which were grown on the area before. Precision agriculture foresees the continuous monitoring of crops and soil for the operational planning of the range of actions to optimize the condition of problem areas. For example, if a separate section of the field area of 20 hectares has a small yellow spot area of 0.5 hectares, it is not necessary to fertilize or to impose additional watering sessions to the whole field – it is enough just to handle problem areas. This will result in much lower costs of fertilizer, POL, wages and depreciation of equipment, even more – it will save working hours of equipment and employees for other tasks.

Monitoring Systems

 Monitoring of fields can be realized in different ways: driving round fields, collecting and analyzing soil samples, using sensors and aerial photography. At the current level of technological development, one can launch aircraft without a pilot but equipped with sensors,  photo- and video cameras and filled with fuel to make a 30-minute flight. However, the complexity of control and maintenance of such equipment, as well as the size of field (over 100 hectares) make this work scheme quite expensive and hardly feasible. For such a scale, agrarians opt for satellite space shooting , the processing of which allows to monitor crops and to make decisions about pointed application of fertilizers, insecticides or herbicides, irrigation or other actions  based on the handling of images with overlaid in red and infrared spectrum. In addition, data from such programs can be uploaded in any electronic device or in the onboard computer of agricultural machinery making it easier to set tasks for employees in the agricultural enterprise.

Satellite crop monitoring systems are successfully used in many countries of America, Europe and the CIS. The most well-known and effective providers of this service are such companies as Cropio (USA/Germany), Astrium-Geo (France), Mapexpert (Ukraine), Vega (Russia). The use of these systems allows not only to monitor efficiently the condition of fields, but also to receive reports and notifications about the most important issues through Internet or sms, to make forecasts of the field productivity and the entire enterprise, to receive related information about the agricultural markets, currency rates and prices for agricultural products in certain markets, to compare current and historical indices of vegetation, soil moisture, content of fertilizers.

Cost Savings Plain to See

Few of us has thought that it takes at least 0.4 liters of fuel or UAH 1.2 to drive round of field area of 1 ha (100 m*100 m) 8 times per year. According to the American Institute of the Precise Farming, the differentiated fertilization brings savings of 10% per hectare. Having summed these and other explicit and implicit costs, we can obtain savings of at least UAH 146 per hectare using satellite observation in Ukrainian agriculture.

If in the Ukrainian realities domestic businessmen are progressive enough and ready to start running the management according to new standards using the techniques of precise agriculture, it is quite possible that eventually Ukraine will become one of the absolute world leaders in the production of some crops, and a number of major agricultural exchanges will be opened on its territory involving customers from around the world. The Ukrainian agronomist who uses services of satellite crop increases its professional efficiency and management methods makes a real “jump” from the Stone Age to the age of high technology. Such an agronomist is in the same league with his colleagues from around the world leveraging not only Soviet scientific school knowledge, but also the global scientific progress.

The result is smaller staff of agronomists, lower fuel and fertilizer costs. Having one or more of such satellite monitoring centres, the agrarian can cut costs that previously put at risk the profitability of enterprise, and what is even more crucially, optimize the quality and return of each resource, be it land, workers, machinery or fertilizers. It is always better to make qualitative changes rather than quantitative ones in each operation of business cycle. The customer is ready to buy the product at a price not higher than a certain threshold which occurs as the average price of all sellers, in a free market he will not overpay for our inability to run a business efficiently. As advertising recalls: “Why should I pay more?”

Summing up, let us recollect another wise saying: “Everything dies without sustained development.” Nowadays it is not enough to own hundreds of hectares of high-quality black soil or endlessly increase fleet of vehicles. Once in a while one should step back and take a look at unproductive attempts to invest and think how to make more money. The authors of political economy put it that the possessing of right information helps to make a profit.

Tags: precise agriculture, Ukraine, satellite, fertilizer, wheat, barley, legumes, sunflower, agronomist, Cropio

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Normalized Difference Vegetation Index

Vegetation is a process of plants growth and development activity.

One of the most popular methods for vegetation level appraisal is Normalized Difference Vegetation Index (NDVI).

NDVI can be calculated using the following formula: NDVI=NIR-RED/NIR+RED, where NIR means near-infrared region reflection, RED – red region reflection. This correlation is based on different spectral features of chlorophyll in a visible and short-range IR range. Modern technology enable us to use objects spectral characteristics, results of textures and colours intensity analysis ; to develop indices and functions on basis of these features.

For a manual probing agrarians use such gadgets as Yara N-Tester, Trimble GreenSeeker and FieldScout. The price range for a gadget lays within $3-5 ths range.

For a automatic probing agrarians use such gadgets as Trimble GreenSeeker and Yara N-Sensor which can be installed on self-propelled agriculture machines. The price range for a gadget lays within $25-40 ths range.

For large agriculture facilities  becomes popular to use satellite crop monitoring. The vegetation level is calculated on the base of each pixel from satellite images. Each field analysis can by displayed as a digital vegetation map. The most popular service providers are Monitoring Agricultural Resources (Italy), Astrium-Geo (France), Cropio (USA/Germany), Vega (Russia).

Tags: vegetation, NDVI, NIR, IR, satellite, Cropio, Mapexpert,Vega, Yara N-Tester, Trimble GreenSeeker, FieldScout, Trimble GreenSeeker, Yara N-Sensor, Normalized Difference Vegetation Index, crop, monitoring

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