Matching technology to value creation: Drones in agriculture

‘Drones in agriculture’ has been a topic hot lately, but is it a real business?

Drones may someday perform all kinds of “work” in the field, like spraying or seeding.  However, today the product of the drone industry is not drones, it’s data. Two ideas persuaded me that there is a business here.   The first is that variability information has real value to growers — it is production control data.  The second is that there are places where drone technology allows real data to replace heuristics and estimation in order to drive action.

Agriculture is a very sophisticated business.  Even in specialty crops with less automation, the growers are extremely sophisticated and as scientifically inclined as one would expect of managers of multi-million dollar production facilities.  Putting production facilities out of doors and using production implements derived from nature only increases the complexity of these operations.   In particular, it introduces uncertainty, variation, and risk into the production process.  The managers of these operations, who  have responsibility for maximizing production, are the customers for this data.

When these managers are trying to squeeze the most production out of a given set of inputs, particularly land and labor, there is a natural cycle of input opportunities.  Data only has value if the manager can act on it, and this is where agriculture becomes extremely heterogeneous.  At one end of the spectrum you have dry farming of corn, where there might be three or four viable input opportunities (plant, a spray or two, then harvest), most of which are already done automatically — or robotically — on a combine.  In high value per acre specialty crops (like vegetables, grapes, or nuts), managers might make over 50 discrete, pre-planned input decisions each year, especially if the crop is deficit irrigated.  Even though these crops are in some ways “lower tech” in that they don’t use as much machine automation (such as combines), they do have a much more sophisticated ability to react to variation data.  These are where the drone’s fundamental advantages are most likely to be valued.

The tradeoff in data collection is a tradeoff between the cost of collection per geographic unit and “responsiveness.”  Responsiveness is the degree to which the system can deliver exactly the data needed to make better decisions, exactly when needed, and is a function of many factors including system reliability, resolution, revisit rate, sensor selection, controllability, and ease of deployment.

Drones and other overhead networked systems provide a continuum of data options.  At one end you have LANDSAT satellite data, which provides free, 30-meter pixels, un-interpreted data once every 16 days (if you are lucky enough not have clouds).  At the other end of the continuum, there are hand-launched drones, which have a high real cost per acre because of their short lifespan and the amount of labor required, but can be launched at anytime and are cheap enough to keep in the back of your truck.  In between you have a variety of commercial solutions.


There will always be a tradeoff between cost and responsiveness, but the idea of drones is that electronics will move the efficient frontier in this tradeoff towards more responsiveness at a given cost.  The challenge with this access to better data is not to overshoot the mark.  It is very expensive to overshoot the required responsiveness for the crop.  The trick is match responsiveness to the needs of the manager.  This is the task for the commercial drone industry: match responsiveness to the needs of the decision-maker.

As time goes on, every category of platform across the continuum will have to offer more responsiveness for the same money.  However, the customer responsiveness needs will not increase unless the managers are also given new mechanisms for taking action. Beyond being used to control fast-spreading crop diseases, I do not know what new markets will open up for drones — perhaps they aren’t in agriculture; the film industry certainly requires even more responsiveness than anything in this field  — but it is going to be fun to discover them.

One of the challenges that I would like to lay down to the ground robotics and manipulation communities is to reduce the cost of making an intervention.  Until it makes production sense to go out into a cornfield 20 or 30 times per season and take an action to improve growing, there won’t be a need for more responsive information.  As interventions become more profitable and crop managers decide to increase the number of interventions, there will be a greater need to automate data collection.  Then we can kick off a virtuous cycle of robotic technology.

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Top trends in agriculture technology

Emerging technology is arguably one of the most significant issues ahead for ag retailers. It is in that spirit that I present my top five technology trends, picked out as being the most important from the perspective of an information technology company.
1. Education. The first and probably the most important topic is education. With the rapid advances in computers and communication, it has become increasingly difficult for anyone in the agricultural community to keep pace with technical changes impacting field production. From understanding the genetics of seed relative to an environment, evaluating the best fertilizers, selecting the right combination of pesticides, or just understanding the day-to-day logistics of a farm operation, education is paramount for success.
In the near future, successful retailers will not be judged by price alone but also according to the materials they recommend in production and their support of sustainability practices on a farm. Consequently, retailers must develop a more “holistic” view of a farming operation. They must understand how each decision through a growing season impacts the next decision and how, collectively, decisions impact the environment and the final produce delivered to the food supply chain. Knowledge of production practices will be demanded not only by regulatory agencies but also be growing segment of the consuming public. The anticipated upstream pressure from the food supply chain can be met with on-farm operation that has accountability and traceability.
A subtler need in education is the modern understanding of integrated pest management (IPM) practices. With the rise of resistant pests, the choice of pest controls must be done more intelligently. Control decisions will require seasonal observations on the presence and movement of pests along with models predicting their future development. Visual observations will give way to diagnostic tests and written records will be replaced by electronic input using applications on smart mobile devices. Retailers that gravitate to these new tools and programs will save time and resources.
2. “Big Data.” Companies involved with the processing and storing of data refer to increasing amounts of computer-generated information as the “big data” challenge. As information becomes critical for good decision-making whether as part of the in-field support of a customer or between employees in a company, data must be collected, stored and interpreted in a timely manner. This data processing requires the maintenance of local computers and communication networks and their integration with systems over the Internet or “in the cloud.” Because of the overhead cost to maintain local computers and networks, many retailers will likely look to “cloud” solutions to process their data. That is, they will pay a fixed annual fee to remotely access computer and network services.
Remote access has the benefit of having a third-party hosting and maintaining the computer and communication infrastructure. However, one big negative is that retailer’s data, because it is stored offsite, will be at the mercy of the third-party’s security solutions. Consequently, a retailer must investigate the security track record of any third-party before signing on to their services.
Assuming a secure computer and communication infrastructure, whether local or remote, the processing of data, themselves, will continue to be a challenge. In order to use data in decision making, a retailer must ingest data, use formulas or models and visualization tools to convert them into usable products, integrate the various products, which can be in the form of text, graphs, tables, or maps, to create an operational “picture” of crop production in the field, and finally interpret this picture for the appropriate decisions during a growing season. Retailers would likely rely on information technology (IT) companies to assist in the processing of data for production decision making.
3. Robotics. Robotics is the use of robots or automated machines in place of humans to process information or to perform physical tasks. When you mention robots, most people think of an assembly line with robot arms constructing cars. But robotics is much more than that. It can be in the form voice recognition software that answers verbal questions from customers. It can be in the form of software that processes data and makes recommendations for production practices. In one sense, this software can be thought of as a “virtual” field manager. In the next few years, software mimicking the decision making of a manager will become increasingly important because of the “big data” challenge. No human can process the flood of data and other information in a timely manner to make a decision. Consequently, retailers will have to rely on software to ingest, convert, and integrate data into products and recommendations. Managers will still make the final decision but at a much higher, informed level by viewing products and recommendations. The same software for processing data can be used to send alerts to field managers about important events, such as severe weather.
The more classical role of robots will also become evident on the farm but at a much slower rate. Autonomous machines that are remotely directed using telematics will begin to appear in a limited role in the next few years. These machines will first be employed in repetitive, simple tasks, such as for the loading and unloading of materials. Eventually, these machines will take the form of autonomous tractors planting seed, spreading fertilizers, applying chemicals, and harvesting crops.

4. Evaluative Metrics. Evaluative metrics are scores and indices that provide feedback to both the retailer and customer that an operation has been successful. It can be as simple as an as-applied map which documents a fertilizer application on a field. Or, it can be an economic calculator that reports the bottom line on a daily basis by keeping track of the costs against the expected revenue from a harvested crop. Evaluative metrics form the basis of the Field-to-Market Fieldprint calculator with its tracking of sustainable practices for land use, soil conservation, soil carbon, irrigation water use, energy, greenhouse gas emissions, and soon to be added water quality.
Besides providing a quantification of success, evaluative metrics can provide measures of efficiency and risk. Decisions, with their subsequent actions and materials, can be organized in terms of crop, soil, weather, and pests. Materials applied in the same way and in the same amounts but in different environmental settings can have different outcomes in terms of a final yield. Retailers who identify which practices and materials work best in producers’ local environmental conditions can contribute to the efficient management of their fields. As one begins to understand what combinations of decisions work or do not work for a particular field and crop, then evaluative metrics can be used to define risk. A set of practices that always result in very good yields (low risk) may be more favorable than using a set of practices that result in fair yields with an occasional high yield (high risk). Evaluative metrics require that good records be kept about the production practices and yields for each field. Furthermore, these records must use a consistent set of variables and observational protocols as part of the collection process. These records would be considered part of the “big data” feed used to asses farming operations.
5. Market Feedback. Market feedback can be in two forms: quantity and quality. Market prices fluctuate based on the real or perceived quantities of produce available for purchase in the marketplace. It is the traditional supply and demand. If there is less produce for purchase, prices go up. If there is more produce, prices go down. Retailers have always been aware of the importance of quantity. However, quality is a little trickier to track. Like a fine wine, it is the combination of the choice of variety, soil, weather and practices. While many times, the quality of a crop is a function of weather, understanding the impact of seed, soil, and practices can give a producer an edge in the marketplace. More oil or protein in beans can translate into more dollars.
Market feedback can help define the end goal for a set of production practices. If the goal is higher protein in a crop, then special attention would be paid to those materials and practices and environmental conditions that meet this goal. As discussed in an earlier topic, evaluative metrics would come in to play to ensure that day-to-day decisions follow a production plan that would realize high-protein yields. Savvy retailers have long realized that while the market is the end point in production, it is the starting point in planning. In a world of better informed buyers, market feedback needs to be front and center for everyone involved in production.

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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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Spectrum of the Pests on Cereal Crops and Influence of Soil Fertilisation

Different biotic factors such as predators, parasitoids and different pathogens affect the pests of cereals. Also affecting them are the abiotic factors temperature, rainfall, humidity, wind and sunshine (Sehgal 2006).

Climatically, the slowly rising temperatures are very important for the spectrum of pests. Winters are shorter, temperatures below freezing occur on average on fewer days than before and the soil is frozen to shallower depths. This results in pest species occurring at northern localities, whereas  before they had appeared only at lower latitudes (Gallo 2002).

Past research has shown that fertilisation of the soil affects a pest’s presence. Fertilisation had a positive effect on the crops, especially on their height and density, leading to a higher presence of pests. Stimulation of plant growth and development is the main goal of fertilisation. Regeneration of the plants is important if the pests damage the plants.

Organic fertilisation is very important because it suppresses the plant pathogens and also affects the frequency of pests (Sokolov 1991). Healthy vital plants are preferred by many pests on cereal species (Price 1991; Breton & Addicott 1992; Preszler & Price 1995).

The variety of a crop can also affect the occurrence of a pest. Ovipositonal behaviour, variability of eggs, growth and survival of the young larvae of the cereal leaf beetle were adversely affected by the trichome density on wheat leaves(Schillinger& Gallun 1968). Eggs per plant and larval survival decreased asthe length and density of trichomes increased (Hoxie et al. 1975).

Differences in the yields between the varieties and the year’s volume in the same level of the damage were detected. Later varieties were better able to compensate for the damage than earlier varieties in years with similar weather (Šedivý 1995).

Influence of a new farming system on pests and their natural enemies has began a few years ago (Khamraev 1990; Pfiffner 1990; Xie et al. 1995).

Material And Methods

Our research was conducted at Nitra-Dolná Malanta. The whole area (530 m2 ) used in the tree-year experiment (2004–2006) was split into 8 identical plots (each of 60 m2 ) separated by a 1 m belt where soil was superficially cultivated.

Pests were monitored on spring barley cvs. Jubilant (2004) and Annabell (2005, 2006), and on winter wheat cvs. Samanta (2004) and Solara (2005, 2006). At every sampling date 5 m2 of winter wheat and spring barley was swept using a standard sweeping net. Collecting began at the shooting phase, ended at wax ripeness of cereal crops and was done every 7–10 days, depending on the weather.

The insects collected from each crop were determined. Spiders were removed from samples, and aphids were excluded from the analysis at they were used in another study. We determined the influence of fertilisation and nutrition on the pests in the control variants(0) without fertilisation, and variants fertilised (F) with manure (40 t/ha) + fertilisers(winter wheat for 7 t yield: 70 kg N/ha, 30 kg P/ha, 0 kg K/ha; spring barley for 6 t yield: 30 kg N/ha, 30 kg P/ha, 0 kgK/ha).Climatic data were provided by the meteorological station nearthe Slovak University of Agriculture at Nitra, Slovak Republic. All results were evaluated with mathematical-statistic analysis Statgraphics.

Results and disscusion

Spring barley

In 2004, insects were first collected in the last decade of April; during next 2 years collecting began in the first decade of May. The beginning of collecting was affected by the weather during the years. The last insects occurred in the first decade of July. The date of the maximum occurrence of the pests changed during the research. Maximum of the insects was recorded between the May and (307pieces/5m2 ) a June (357pieces/5 m2) in 2004.The abundance of the insects decreased at about 45% after this time. The lowest abundance of insects was in May during the low average day temperatures. Maximum occurrence of total insects was in the last decade of May and in the first decade of June. Our results are similar to results of Gallo (2002), who maximum of the insects recorded in the first decade of May and June. Only one maximum of occurrence was recorded in 2004.Only one maximum was recorded in 2006 too. Two maximum of occurrence were recorded in 2005.

The maximum occurrence of the pests(89 pieces/5 m2 ) was recorded on spring barley at the beginning of the June in 2006. Phyllotreta (18 pieces/5m2 ), Thysanoptera (21 pieces per 5 m2 ) and Oulema gallaeciana (12 pieces/5 m2 )were dominant pests. Other authors presented the same spectrum of dominant pests (Gallo & Pekár 1999). There were no insects recorded on spring barley in July. The spring barley was in the wax ripeness. Abundance of the natural enemies increased at about 28% in the second decade of June but it decreased compared to the first decade about 80% by the end of month.

Different occurrence of the insectswasin20.The maximum of the collected insects (102 pieces per 5 m2 ) was at the end of June. The total number of insects 114 pieces/5 m2 was recorded in the first decade of June and from which there were 84pieces/5m2 pests. There was recorded maximum amount of natural enemies(14 pieces/5 m ). The second maximum of total insects(262 pieces/5m2) and also pests (177 pieces/5 m2 ) was recorded in the last decade of May. The collecting was realised in the first decade of July in 2005. The amount of the pests decreased at about 34% and natural enemies at about 63% in 2005. During the three years study the effect of fertilisation was monitored in the spring barley (Figure 2).According the results, more pests were recorded on fertilised variants.The more pests were recorded on the non-fertilised variants only in 2004. Our results are different from results of Samsonova (1991), according which the fertilisation had no effect on the occurrence of the pests. Levine (1993) results are similar to ours.

The occurrence of the insects had the similar character on fertilised and non-fertilised variants. The relation between fertilised and non-fertilised variants had not statistically significant difference

Total amount of collected insects on spring barley had increasing tendency during the all years. Total amount of the pests was 1630 pieces per 5 m2 collected during the entire period in2004.

This number decreased at about 55%in 2006.The amount of natural enemies had also decreasing tendency. While 66 pieces/5 m2 of natural enemies were collected in 2004, this amount decreased at about 67% in 2006 (Table 1). This drop could be caused by higher temperature during the year 2006.

According to Honěk (2003), McAvoy and Kok (2004), the temperature influenced occurrence and development of the pests in crops. Difference between the year 2004 and the other years 2005 and 2006 was statistically evident.

Winter wheat

The beginning of collecting was realised in the second decade of April and the last collections were realised in the first decade of July during the years 2004–2006.The last collection was realised atthe end of June in 2006.The beginning was affected by the weather during the years.

The first maximum of the pests was recorded in the last decade of April(247 pieces/5 m2 )in 2004. The second maximum was recorded at the end of May (350 pieces/5 m2 ) and at the beginning of June(285 pieces/5 m2 ).Thrips (44 pieces/5 m2 ) had the highest occurrence. The cereal leaf beetle was recorded at most in April. It was found occasionally in the crops in the next time. Aphids(58 pieces/5 m2) were recorded in the crops in June. Maximum occurrence of the pests was recorded in the last decade of May (284 pieces/5 m2 ), the second one in the second decade of June (314 pieces/5 m2 ) in 2005. There were trips (26 pieces/5 m2 ) and flea beetle (38 pieces/5 m2 ) and also cereal leaf beetle(15 pieces/5 m2).The aphids were not recorded in this year. During the terms with the highest level of the pests, there were also recorded the highest occurrence of the natural enemies (53pieces/5 m2).

Our results were similar to the results of Gallo and Pekár (1999, 2001),which presented the same spectrum of the pests. The high occurrence of the pests was recorded also in the first(266 pieces/5 m2) and in the second (197 pieces/5 m2 ) decade of June. The highest occurrence had again thrips (18 pieces per 5 m2 ), flea beetle (24 pieces/5 m2) and cereal leaf beetle (10 pieces/5 m2). Frittfly Oscinella fritt was recorded (6 pieces/5 m2) during the entire year.

The higher occurrence ofthe pests could be caused by higher temperature during the year. According to Petr et al. (1987), and McAvoy and Kok (2004), the temperature influenced occurrence and development of the pests in the crops. The influence of the fertilisation was study during three years research on winter wheat.

According to our results, the higher occurrence of the pests was recorded on fertilised variants. The higher occurrence of the pests was recorded on the non-fertilised variants only in the year 2004. Our results are different from the results of the other author according which the fertilisation had no influence at the pests (Samsonova 1991).The similar results reached Levine (1993) and Gallo and Pekár (1999).The occurrence of the insects had the similar character on fertilised and non-fertilised variants. Maximum amount of the pests was recorded one decade sooner on the fertilised variants than on the non-fertilised variants. Relation between fertilised and non-fertilised variants had not statistically significant difference.

The total number of insects had a decreasing tendency during the three years and in both variants of fertilisation. While 3259/5 m2 of insects were collected in 2004, this number decreased by about 51% in 2006. This decrease was expressive on the non-fertilised variantsin 2006, it began on the fertilised variants after the first year. The same decreasing tendency had also pests and their natural enemies. Their occurrence decreased at about 59%during the study. Difference between the year 2006 and the other years 2005 and 2004 was statistically evident.

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Biological Control and Natural Enemies

Biological control is the beneficial action of predators, parasites, pathogens, and competitors in controlling pests and their damage. Biocontrol provided by these living organisms (collectively called “natural enemies”) is especially important for reducing the numbers of pest insects and mites. Natural enemies also control certain rangeland and wildland weeds, such as Klamath weed (St. Johnswort). Plant pathogens, nematodes, and vertebrates also have many natural enemies, but this biological control is often harder to recognize, less-well understood, or more difficult to manage. Conservation, augmentation, and classical biological control (also called importation) are tactics for harnessing the effects of natural enemies.


Predators, parasites, and pathogens are the primary groups used in biological control of insects. Most parasites and pathogens, and many predators, are highly specialized and attack only one or several closely related pest species.


Pathogens are microorganisms including certain bacteria, fungi, nematodes, protozoa, and viruses that can infect and kill the host. Populations of some aphids, caterpillars, mites, and other invertebrates are sometimes drastically reduced by naturally occurring pathogens, usually under conditions such as prolonged high humidity or dense pest populations. In addition to naturally occurring disease outbreaks, some beneficial pathogens are commercially available as biological or microbial pesticides. These include Bacillus thuringiensis or Bt, entomopathogenic nematodes, and granulosis viruses. Additionally, some microorganism by-products such as avermectins and spinosyns are used in certain insecticides, but applying these products is not considered to be biological control.


A parasite is an organism that lives and feeds in or on a larger host. Insect parasites (more precisely called parasitoids) are smaller than their host and develop inside, or attach to the outside, of the host’s body. Often only the immature stage of the parasite feeds on the host, and it kills only one host individual during its development. However, adult females of certain parasites (such as many wasps that attack scales and whiteflies) feed on their hosts, providing an easily overlooked but important source of biological control in addition to the host mortality caused by parasitism.

Most parasitic insects are either flies (Diptera) or wasps (Hymenoptera). Parasitic Hymenoptera occur in over three dozen families. For example, Aphidiinae (a subfamily of Braconidae) attack aphids. Trichogrammatidae parasitize insect eggs. Aphelinidae, Encyrtidae, Eulophidae, and Ichneumonidae are other groups of tiny size to medium-sized wasps that parasitize pests but do not sting people. The most common parasitic flies are Tachinidae. Adult tachinids often resemble house flies. Their larvae are maggots that feed inside the host.


Insects are important food for many amphibians, birds, mammals, and reptiles. Many beetles, true bugs (Hemiptera or Heteroptera), flies, and lacewings are predators of various pest mites and insects. Most spiders feed entirely on insects. Predatory mites that prey primarily on spider mites include Amblyseius spp., Neoseiulus spp., and thewestern predatory mite (Galendromus occidentalis).

Recognizing Natural Enemies

Proper identification of pests, and distinguishing pests from their natural enemies, are essential to effectively using biological control. For example, some people may mistake aphid-eating syrphid fly larvae for caterpillars. The adult syrphid, commonly also called a flower fly or hover fly, is sometimes mistaken for a honey bee. Consult publications such as the UC Statewide Integrated Pest Management Program Pest Notes series listed in Suggested Reading to learn more about the specific pests and their natural enemies in your gardens and landscapes. Take unfamiliar organisms you find to your Cooperative Extension office or county agriculture commissioner for an expert identification. Carefully observe the creatures on your plants to help discern their activity. For example, to distinguish plant-feeding mites from predaceous mites, observe them on your plants with a good hand lens. Predaceous species appear more active than plant-feeding species. In comparison with pest mites, predaceous mites are often larger and do not occur in large groups.


Preserve naturally occurring beneficial organisms whenever possible. Most pests are attacked by several different types and species of natural enemies, and their conservation is the primary way to successfully use biological control in gardens and landscapes. Ant control, habitat manipulation, and selective pesticide use are key conservation strategies.

Pesticide Management

Broad-spectrum pesticides often kill a higher proportion of predators and parasites than of the pest species they are applied to control. In addition to immediately killing natural enemies that are present (contact toxicity), many pesticides are persistent materials that leave residues that kill natural enemies that migrate in after spraying (residual toxicity). Residues often are toxic to natural enemies long after pests are no longer affected. Even if beneficials survive an application, low levels of pesticide residues can interfere with natural enemies’ reproduction and their ability to locate and kill pests.

Biological control’s importance often becomes apparent when broad-spectrum, persistent pesticides cause secondary pest outbreaks or pest resurgence. A secondary outbreak of a different species occurs when pesticides applied against a target pest kill natural enemies of other species, causing the formerly innocuous species to become pests. An example is the dramatic increase in spider mite populations that sometimes results after applying a carbamate (e.g., carbaryl or Sevin) or organophosphate (malathion) to control caterpillars or other pests.

Eliminate or reduce the use of broad-spectrum, persistent pesticides whenever possible. Carbamates, organophosphates, and pyrethroids are especially toxic to natural enemies. When pesticides are used, apply them in a selective manner. Treat only heavily infested spots instead of entire plants. Choose insecticides that are more specific in the types of invertebrates they kill, such as Bacillus thuringiensis (Bt) that kills only caterpillars that eat treated foliage. Rely on insecticides with little or no persistence, including insecticidal soap, horticultural or narrow-range oil, and pyrethrins.

A less-persistent pesticide can result in longer control of the pest in situations where biological control is important because the softer pesticide will not keep killing natural enemies. One soft pesticide spray plus natural enemies can be effective for longer than the application of one hard spray.

Ant Control and Honeydew Producers

Ants are beneficial as consumers of weed seeds, predators of many insect pests, soil builders, and nutrient cyclers. Ants may attack people and pets or are direct pests of crops, feeding on nuts or fruit (See Pest Notes: Red Imported Fire Ants). The Argentine ant and certain other species are pests primarily because they feed on honeydew produced by Homopteran insects such as aphids, mealybugs, soft scales, and whiteflies. Ants protect honeydew producers from predators and parasites that might otherwise control them. Ants sometimes move these honeydew-producing insects from plant to plant. Where natural enemies are present, if ants are controlled, populations of many pests will gradually (over several generations of pests) be reduced as natural enemies become more abundant. Control methods include cultivating soil around ant nests, encircling trunks with ant barriers, and applying insecticide baits near plants. See Pest Notes: Ants for more information.

Habitat Manipulation

Manage gardens and landscapes by using cultural and mechanical methods that enhance natural enemy effectiveness. Grow diverse plant species and tolerate low populations of plant-feeding insects and mites so that some food is always available to retain predators and parasites. Plant a variety of sequentially flowering species to provide natural enemies with nectar, pollen, and shelter throughout the growing season. The adult stage of many insects with predaceous larvae (such as green lacewings and syrphid flies) and many adult parasites feed only on pollen and nectar. Even if pests are abundant for the predaceous and parasitic stages, many beneficials will do poorly unless flowering and nectar-producing plants are available to adult natural enemies. Reduce dust, for example, by planting ground covers and windbreaks. Dust can interfere with natural enemies and may cause outbreaks of pests such as spider mites. Avoid excess fertilization and irrigation, which can cause phloem-feeding pests such as aphids to reproduce more rapidly than natural enemies can provide control.


When resident natural enemies are insufficient, their populations can sometimes be increased (augmented) through the purchase and release of commercially available beneficial species. However, there has been relatively little research on releasing natural enemies in gardens and landscapes. Releases are unlikely to provide satisfactory pest control in most situations. Some marketed natural enemies are not effective. Praying mantids, often sold as egg cases, make fascinating pets. But mantids are cannibalistic and feed indiscriminately on pest and beneficial species. Releasing mantids does not control pests.

Only a few natural enemies can be effectively augmented in gardens and landscapes. These include entomophagous nematodes, predatory mites, and perhaps a few other species. For example, convergent lady beetles (Hippodamia convergens) purchased in bulk through mail order and released in very large numbers at intervals can temporarily control aphids; however, lady beetles purchased through retail outlets are unlikely to be sufficient in numbers and quality to provide control.

Successful augmentation generally requires advanced planning, biological expertise, careful monitoring, optimal release timing, patience, and situations where certain levels of pests and damage can be tolerated. Desperate problems where pests or damage are already abundant are not good opportunities for augmentation.


Classical biological control, also called importation, is primarily used against exotic pests that have inadvertently been introduced from elsewhere. Many organisms that are not pests in their native habitat become unusually abundant after colonizing new locations without their natural controls. Researchers go to the pest’s native habitat, study and collect the natural enemies that kill the pest there, and then ship promising natural enemies back for testing and possible release. Many insects and some weeds that were widespread pests in California are now partially or completely controlled by introduced natural enemies, except where these natural enemies are disrupted, such as by pesticide applications or honeydew-seeking ants.

Natural enemy importation by law must be done only by qualified scientists with government permits. Natural enemies are held and studied in an approved quarantine facility to prevent their escape until research confirms that the natural enemy will have minimal negative impact in the new country of release. Because classical biological control can provide long-term benefits over a large area and is funded through taxes, public support is critical for continued success. Consult Natural Enemies Handbook and Pests of Landscape Trees and Shrubs to learn about situations where imported natural enemies are important and conserve them whenever possible.

Is Biological Control “Safe”?

One of the great benefits of biological control is its relative safety for human health and the environment. Most negative impacts from exotic species have been caused by undesirable organisms contaminating imported goods, by travelers carrying in pest-infested fruit, by introduced ornamentals that escape cultivation and become weeds, and by poorly conceived importations of predatory vertebrates like mongooses. These ill-advised or illegal importations are not part of biological control. To avoid these problems, biological control researchers follow regulations and work with relatively host-specific insects.

Help preserve our environment and avoid introducing exotic new pests.

Do not bring uncertified fruit, plants, or soil into California. Take unfamiliar pests to your county agricultural commissioner or Cooperative Extension office for identification.


Although many animals prey on pest insects or mites, not all can be relied upon to reduce a pest population enough to protect plants. The most effective natural enemies are often relatively host specific, feeding on a single pest species or a group of similar pests such as aphids or scales. Good examples include predatory mites, most parasitic wasps, and syrphid flies. Very general predators such as praying mantids are often likely to kill as many beneficials as pests and thus rarely provide effective control.

Synchronization of the life cycle and environmental requirements of the pest and natural enemy also determine the effectiveness of biological control. Natural enemies that do not arrive or become abundant until after pests are very abundant may not prevent serious damage to plants. Conversely, a parasite or predator with multiple annual generations, that can attack a broad range of life stages of the pest and can feed and reproduce when pest populations are low or moderate, will likely be a more effective natural enemy.

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Automatic Steering of Farm Vehicles Using GPS

Autonomous guidance of agricultural vehicles is not a new idea, however, previous attempts to control agricultural vehicles have been largely unsuccessful due to sensor limitations. Some control systems require cumbersome auxiliary guidance mechanisms in or around the field while others rely on a camera

system requiring clear daytime weather and field markers that can be deciphered by visual pattern recognition. With the advent of affordable GPS receivers, engineers now have a low-cost sensor suitable for vehicle navigation and control.

GPS-based systems are already being used in a number of land vehicle applications including agriculture. Meter-level code-differential techniques have been used for geographic information systems, driver-assisted control, and automatic ground vehicle control.

Using precise differential carrier phase measurements of satellite signals, CDGPS-based systems have demonstrated centimeter-level accuracy in vehicle position determination  and 0.1˚ accuracy in attitude determination .

System integrity becomes impeccable with the addition of pseudo-satellite Integrity Beacons. The ability to accurately and reliably measure multiple states makes CDGPS ideal for system identification, state estimation, and automatic control. CDGPS-based control systems have been utilized in a number of applications, including a model airplane, a Boeing 737 aircraft, and an electric golf cart.

This paper focuses on the automatic control of a farm tractor using CDGPS as the only sensor of vehicle position and attitude. An automatic control system was developed, simulated in software using a simple kinematic vehicle model, and tested on a large farm tractor.

The primary goal of this work was to experimentally demonstrate precision closed-loop control of a farm tractor using CDGPS as the only sensor of vehicle position and attitude. This section describes the hardware used to do this.

Vehicle Hardware

The test platform used for vehicle control testing was a John Deere Model 7800 tractor (Fig. 1). Four single-frequency GPS antennas were mounted on the top of the cab, and an equipment rack was installed inside the cab. Front-wheel angle was sensed and actuated using a modified Orthman electro-hydraulic steering unit. A Motorola MC68HC11 microprocessor board was the communications interface between the computer and the steering unit.

The microprocessor converted computer serial commands into a pulse widthmodulated signal which was then sent through power circuitry to the steering motor; the microprocessor also sampled the output of a feedback potentiometer, the only non-GPS sensor on the vehicle, attached to the right front wheel. The 8-bit wheel angle potentiometer measurements were sent to the computer at 20 Hz. through the serial link.

GPS Hardware

The CDGPS-based system used for vehicle position and attitude determination was identical to the one used by the Integrity Beacon Landing System (IBLS). A four-antenna, six-channel Trimble Vector receiver produced attitude measurements at 10 Hz. A single-antenna nine-channelTrimble TANS receiver produced carrier- and code-phase measurements at 4 Hz. which were then used to determine vehicle position. An Industrial Computer Source Pentium-based PC running the LYNX-OS operating system performed data collection, position determination, and control signal computations using software written at Stanford.

The ground reference station  consisted of a Dolch computer, a single-antenna nine-channel Trimble TANS receiver generating carrier phase measurements, and a Trimble 4000ST receiver generating RTCM code differential corrections. These data were transmitted at 4800 bits/sec through Pacific Crest radio modems from the ground reference station, which was approximately 800 m from the test site, to the tractor.


Performing a valid tractor simulation required a good model of dynamics and disturbances. Ground vehicle models in the literature range from simple to complex, and no single model is widely accepted. The most sophisticated models are not always appropriate to use , especially since controller and estimator design require a simple, typically linearized, model of plant dynamics.

Kinematic Model

The simplest useful model for a land vehicle is a kinematic model, which is based on geometry rather than inertia properties and forces. Assuming no lateral wheel slip, constant forward velocity, actuation through a single front wheel, and a small front wheel angle, the latter two equations of motion can easily be derived.  The kinematic equations were derived in state-space form for ease of controller and estimator design. The state vector is composed of the lateral position deviation from a nominal path, heading error, and effective front wheel angle.

Steering Calibration

Initially, calibration tests were used to create two software-based “look-up” tables, one which linearized the output of the steering potentiometer versus the effective front wheel angle and the other which linearized the computercommanded wheel-angle rate to the actual wheel-angle rate. To calibrate the potentiometer readings of effective front wheel angle, steady turn tests were performed to find the heading rate (dY/dt) of the tractor at various potentiometer readings. For each test, the tractor was driven in a circular path with a constant front wheel angle and constant forward velocity while GPS heading data was taken and stored. By compiling all these tests, a function was generated that related steady-state heading rate to potentiometer reading.

Calibration of the commanded wheel angle rate was simpler. Constant steering slews were commanded by the computer at varying levels of actuator authority (u) while wheel angle data was taken and stored. The time rate of change of the effective wheel angle was later estimated for each steering slew.


The first controller designed, simulated, and tested on the tractor performed closed-loop heading. The computer code was written so a user could command a desired heading using a keyboard input. The computer would then send the appropriate commands to the electro-hydraulic actuator to track the desired heading. The first tests were closed-loop heading tests designed to verify the kinematic vehicle model. These initial tests also yielded a better feel for tractor disturbances.

Heading Controller Design

A hybrid controller was designed to provide a fast response to large desiredheading step commands. A non-linear “bang-bang” control law generated actuator commands when there were large errors or changes in the vehicle heading or effective wheel angle states. Typically, these large changes occurred in response to a large heading step command. When the vehicle states were close to zero, a controller based on standard Linear Quadratic Regulator (LQR) design  was used.

“Bang-bang” control is a standard non-linear control design tool based on phase-plane technique. Unlike linear feedback controllers, bang-bang controllers use the maximum actuator authority to zero out vehicle state errors in minimum time just as a human driver would. For example, in response to a ,commanded heading step increase of 90˚, a bang-bang controller commands the steering wheel to hard right, holds this position, and then straightens the wheels in time to match the desired heading. In contrast, a linear controller would respond to the step command by turning the wheels to hard right, then slowly bringing them back to straight, asymptotically approaching the desired heading.

The drawback to bang-bang control is that when state errors are close to zero, the controller tends to “chatter” between hard left and hard right steering commands. For this reason, a linear controller was used for small deviations about the nominal conditions.

Experimental Heading Results

During the heading tests, the tractor was driven over a bumpy field at a nearly constant velocity of 0.9 m/s. The driver commanded an initial desired heading and a number of desired heading step commands through the computer. The tractor tracked the commanded headings very accurately, even in the presence of ground disturbances. Figure 5 shows a plot of CDGPS heading measurements during the longest closed-loop heading trial recorded. Over about one minute, the mean heading error was 0.03˚ and the standard deviation was 0.76˚. From separate tests, the expected sensor noise was zero mean with approximately 0.1˚ standard deviation, so the true system heading error standard

deviation was almost certainly less than 1˚.

The rise time of the controller for this particular command (response for a 90˚ step in commanded heading ) was approximately 7 seconds, and the settling time was less than 10 seconds. An small overshoot of about 4˚ occurred at the end of the heading step response.


After performing closed-loop heading, the next step toward farm vehicle automation was straight-line tracking. These series of tests were designed to simulate tracking a row. To track a straight line, vehicle position was fed back to the control system along with heading and effective wheel angle.

Line Tracking Controller Design

As in the closed-loop heading case, the line tracking controller was implemented as a hybrid controller with various modes. To get the vehicle close to the beginning of the “field” and locked on to each line or “row”, a coarse control mode was used based on the closed loop heading controller described above. Once a line was acquired, a precise linear controller based on LQR techniques took over.

Experimental Line Tracking Results

Two line-tracking tests were performed on the same field as the closed-loop heading experiments. The vehicle forward velocity was manually set to first gear (0.33 m/s), and the tractor was commanded to follow four parallel rows, each 50 meters long, separated by 3 meters. Throughout these tests, the steering control for line acquisition, line tracking, and U-turns was performed entirely by the control system. CDGPS integer cycle ambiguities were initialized by driving the tractor as closely as possible to a surveyed location and manually setting the position estimate.

In fact, there was a small, steady position bias (about 10 cm) between the two trials due to the unsophisticated method that was used for GPS carrier phase integer cycle ambiguity resolution. A more sophisticated method involving pseudolites or dual frequency receivers would have eliminated this bias and is a topic of future research.

Since the plots show CDGPS measurements and not “truth”, they represent the error associated with the control system and physical vehicle disturbances. The tractor controller was able to track each straight line  with a standard deviation of better than 2.5 cm., the vehicle lateral position error never deviated by more than 10 cm, and the mean error was less than 1 cm for every trial.


This research is significant because it is the first step towards a safe, low-cost system for highly accurate control of a ground vehicle. The experimental results presented in this paper are promising for several reasons. First, a farm tractor control system was demonstrated using GPS as the only sensor for position and heading. Only one additional sensor—the steering potentiometer—was used by the controller. Second, a constant gain controller based on a very simple vehicle model successfully stabilized and guided the tractor along a straight, predetermined path. Finally, it was found that a GPS controller could guide a tractor along straight rows very accurately. The lateral position standard deviation was less than 2.5 cm. in each of the 8 line tracking tests performed Transitioning from automatic control of a lone farm tractor to automatically controlling the same tractor towing an implement is a large step since the

combined system will have more complex dynamics and larger physical disturbances acting on it. Guiding a vehicle along curved paths will also present a challenge that has not been addressed. This work describes a control methodology that was successfully employed to control a real farm tractor to high accuracy. This same methodology, combined with a more sophisticated dynamic model may be sufficient to control the more complicated tractor-implement system. Further research is currently underway to explore this possibility.

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 The increase in ionized radiation and pollution of our environment with herbicides, pesticides, heavy metal compounds, and other toxic mutagenic and carcinogenic substances presents a real danger to living organisms today and their progeny in the future.  Considering the soil pollution by water soluble heavy metal salts in the industrial regions and the long-term excessive use of mineral fertilizer, pesticides, and herbicides in agricultural regions, the crops, particularly vegetables and root-crops, accumulate excess amounts of harmful admixtures.  That is why the creation of pure agricultural technologies is one of the most important tasks of our time.

The protective effect of humates develop in the following directions:

  1. Protection from radioactive irradiation and its consequences.

  2. Protection from harmful admixtures in the atmosphere, soil, and subsoil waters in technogenic districts.

  3. Protection from the consequences of the pesticides and other chemicals used in agriculture.

  4. Protection from unfavorable environmental factors in zones of risky agriculture.

  5. Decrease in content of the nitrates that form when nitrogen fertilizer is used.

     Long-term research showed that humic substances bond many organic and non-organic substances into poorly soluble or insoluble compounds, which prevents their penetration from soil into subsoil waters and growing plants.  It reduces the toxic effect of residual amounts of herbicides, soil polluting radio nuclides, heavy metals, and other harmful substances, as well as radiation and chemical contamination.  Tests showed that even after 50% affection of the plant, its vital functions are completely restored due to the humic preparation effect.  This unique quality of humates is particularly important for the regions in Russia, Byelorussia, and Ukraine that are contiguous to the Chernobyl region.   In the future it could be used to gradually restore contaminated land.

     Modern floriculture is not possible without the use of different chemicals necessary to fight weed, pest, and plant disease.  It is widely known, however, that the use of those chemicals causes a number of negative effects due to their accumulation in the soil.  The infamous fact of DDT accumulation led to its complete banning.  However, DDT appearance still occasionally occurs in crops.  Science proved that sodium humate reduces the damaging effect of the pesticide atrazine by increasing its decomposition, which leads to an increase in the crop capacity of barley.

     The use of humates in zones of risky agriculture is particularly important.  Unfortunately, most territories of Russia can be considered risky.  In the south, the humates help to fight the effect of droughts, since it has been established that the humate treatment of plants ensures their drought resistance.  In Siberia and in the north of Russia, humate treatment can save the plants from late frosts.  In the 1960s, a corn crop was saved by colleagues of Irkutsk university, after an unexpected frost.  In 1996, in the Angarsk region, a strong frost happened on the 19th of June.  The parts of the potato fields that had been treated with the humates were the only undamaged parts.

     Watering soil with a 0.01% humate solution substantially increases the biological activity of the soil and boosts plants resistance against the harmful waste in technogenic zones of chemical and coking industries.  In 1998, in Buryatia, wide scale tests were carried out in treating of saline soils with humates.  The results showed a 214% increase in crops of green herbage, in comparison with the control group.

     The ability of humates to create complexes and their high sorption activity are used to bond the ions of heavy metals in contaminated soil.  That is why increased amount of humates (up to 20-30 kg per hectare) should be used on contaminated soil to ensure the contact and create favorable conditions for forming of complexes.

Humates accelerate water-exchange processes and physiological processes in the cell and participate in oxidation processes at the cell level.  They are conducive to complete assimilation of mineral nutrients in the plant, particularly in abnormal cases, such as saline soils, drought, and other unfavorable environmental factors. 

     An important quality of humates is their ability to decrease the level of nitrate nitrogen in produce.  It was proven by tests on a variety of crops (oats, corn, potatoes, root-crops, lettuce, cucumbers) that humate use decreases the nitrate content by 50% on average.  At the Dnepropetrovsk agricultural institute, field tests were carried out on chernozem soils.  Two crop cultures were tested – corn and barley (as second in the crop rotation).  The herbicide atrazine (4 kg per hectare) was used on the corn.  The results showed that atrazine reduced the growth of weeds by 80% and increased the crop capacity of the corn by 19%-20%.  However, the residual amounts of  the herbicide reduced the crop capacity in barley, which was sown after the corn in crop rotation, by 16%.  The use of sodium humate considerably changed the situation.  It stimulated corn growth and increased the crop capacity by an additional 10%, while the nitrates content (NO3) in the corn of honey and pearl ripeness decreased from 280.1 mg/kg to 199.7 mg/kg in laboratory tests and to 707 mg/kg in field tests.  Barley grown after the corn was noted to improve its germination, growth, and mass gaining, while containing less atrazine and more chlorophyll in the leaves.  The crop capacity of the barley increased by 5.2 centner per hectare, with a total crop capacity reaching 30.9 centner per hectare.  It was also noted that the atrazine content in the final produce decreased by 52%-71%, which made it an ecologically pure produce.

Thus, humic preparations are the reliable protection for plants and crops against harmful admixtures from our environment (soil, subsoil waters, rain-water, and the atmosphere), which is more polluted each day.  They also protect crops from unfavorable environmental factors (drought, ionizing radiation, etc.).

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Humates and chemical fertilizers

 Intensive agricultural systems demand the use of large quantities of mineral fertilizers in order to supply the plants with basic micro-elements, such as nitrogen, phosphorus, and potassium.  In doing so, we often forget that mineral fertilizer is for plants what illegal drugs are for sportsmen – you can immediately see high results but tend to ignore the future consequences.  The higher the amount of mineral fertilizer used, the more intensive is the erosion of the soil, the poorer the soil’s humus content, and the environment is more polluted.  The problem of effective mineral fertilizer assimilation is central in plant-growing.  The difficulty of its solution lies in the fact that water soluble potassium and nitrogen fertilizers are easily washed out of the soil, while phosphorus fertilizers, on the contrary, bond with ions of Ca, Mg, Al, and Fe that are present in soil and form inert compounds, which are inaccessible to plants.  The presence of humic substances, however, substantially increases effective assimilation of all mineral nutrition elements.  It was shown in the tests of barley that humate treatment (with NPK) improved its growth, development, and the crop capacity while decreasing the use of mineral fertilizer. (V. Kovalenko, M. Sonko, 1973.)  The tests on wheat showed that one-way use of nitrogen fertilizers on winter wheat crops did not have a high positive effect on the crop capacity, while its use along with humates and super phosphate achieved an expected positive effect. (L. Fot, 1973.)  Interestingly, the mechanism of interaction between humates and micro-elements of mineral nutrition is specific for each of them.  The positive process of Nitrogen assimilation occurs due to an intensification of the ion-exchange processes, while the negative processes of “nitrate” formulation decelerates.  Potassium assimilation accelerates due to a selective increase in the penetrability of cell membranes.  As for phosphorus, humates bond ions of Ca, Mg, and Al first, which prevents the formation of insoluble phosphates.  That is why the increase of humate content leads to an increase of the plant’s phosphorus consumption. (Lee & Bartlett, 1973.)

Therefore, the combination of humates and mineral fertilizer guarantees their effective assimilation by plants.  

     Thus, the idea of combined use of humates and mineral fertilizer naturally comes to mind.  Creation of such a combined fertilizer is a new step in plant-growing development.  It was no coincidence when over ten years ago an Italian company, “ Vineta Mineraria,” published a project, ”Umex: a new technological tool at service for agriculture of 2000.”  This project was about establishing the production of humate-coated granulated nitrogen, potassium, and phosphorus fertilizers.  From 1988 to 1990, in Byelorussia, the vegetation field tests and production experiences were carried out to comparatively study new humate-coated forms of mineral fertilizers, such as urea, super phosphate, and potassium chloride, produced in Italy and Russia.  The tests showed that use of humate-coated urea in the production experiences with potatoes increased the crop capacity by an average of 28-31 centner/hectare, whilst at the same time decreasing the nitrate content by 40%, in comparison with the control group (urea). For root-crops, the crop capacity reached 200-220 centner/hectare, with an improvement in the quality of the produce. However, in spite of the impressive results, this project was not developed further, and these new preparations did not appear on the international markets.  Perhaps, the high cost of the humates, in comparison with the mineral base, was the reason, so the new type of fertilizer was not competitive.  However, with the new manufacturing technologies today, these materials can be cost-effective in modern agriculture.

     Field tests (M. Butyrin, 1996) showed that use of humate-coated urea increased the crop capacity of potatoes by 20% and that of oats  by 50%.

     Other important components of plants’ nutrition are micro-elements – Fe, Cu, Zn, B, Mn, Mo, Co.  Plants use a very small amount of them, measured in one thousandth or one hundred thousandth of a percent.  Nevertheless, they are vital to plants’ development.  For instance, boron resists certain diseases and increases the amount of ovaries and vitamin content in fruit.  Manganese is vital for the photosynthesis process and the formulation of vitamin C and sugars.  Copper assists in albumen synthesis, which ensures drought and frost resistance in plants, as well as their resistance to fungal and viral infections.  Zinc is part of many vegetable ferments participating in fertilization, breathing, albumen, and carbohydrates synthesis.  Molybdenum and cobalt are important to nitrogen assimilation from the atmosphere.  Considering what was said in previous chapters, the readers might pay attention to our explanations of similar effect.  We explained it was due to humate use.  But if you consider that the humates transport micro-elements to plants most efficiently and form complexes with micro-elements that are easily assimilated by plants, the seeming contradiction is easily resolved.

     Humic acids form complexes naturally.  For thousands of years, they accumulated vital elements.  When applied, humic acids also extract these vital elements from the soil in an accessible way for plants to form.  For example, iron and manganese, according to respected professor D. Orlov, are assimilated only in humic complex form.  Research by A. Karpukhin showed that the presence of these complexes determine the mobility of most macro- and micro-elements and their supply and travel inside plants’ organs.

Therefore, treating vegetating plants with humates ensures their continuous nutrition with vital macro- and micro-elements.

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“More” compared with…?

There is obviously a wide variety of no-till farming systems and so there is an equally wide variety of conventional tillage based agricultural systems. The use of herbicides is a common feature and widespread practice in many intensive farming systems. This applies equally to tillage based conventional farming as to no-till farming. Herbicides are a useful tool for weed management, particularly in the first years after shifting from conventional farming to no-till farming. It is much easier, to do no-till farming with herbicides than without.  If now no-till farming is introduced in an environment of traditional peasant farming, where no herbicides are used at all, these no-till farming systems will obviously use “more” herbicides than the traditional conventional systems.

However, in many conventional systems herbicides are already frequently used and mechanical weed control has nearly disappeared in intensive farming. In such a system, the shift to no-till farming might not necessarily increase the use of herbicides dramatically. Even where it does increase the amount of active ingredient applied per area and year, the environmental impact is not necessarily worse, as often there is a shift from herbicides with relatively high environmental impact to other herbicides with less impact.

Therefore, it is difficult to generalize and no-till farming systems might not always require more herbicides than conventional farming systems.

What are the conditions for increased herbicide use under no-till?

Nevertheless, most of the scientific literature shows that notill farming does in fact require more herbicides than conventional systems comparing similar cropping systems.

There is no doubt that there are significant areas under notillage systems, where herbicide overuse is creating environmental problems. These systems are characterized by monocultures and, in absence of soil tillage, by herbicide use being the only weed management strategy applied. These areas are the ultimate proof for the statement, that no-till farming uses more herbicides. Many of these areas are also cropped with genetically modified crops, which are resistant to a specific herbicide. Therefore, the herbicide use in these cases is restricted to a single product. However, under such a condition, even soil tillage would not really improve the herbicide use. Such cropping systems, with or without tillage, can be considered as not conforming to good agricultural practice.

What is the weed control effect  of tillage?

Soil tillage has been developed for a number of reasons, such as to facilitate the preparation of a seedbed for a more efficient seeding. However, weed control has always been attributed to soil tillage and, particularly, the development of the mouldboard plough was very effective for weed management. But, in the long term, the weed control effect of tillage has proven to be insufficient and herbicides have become the tool of choice in intensive farming. The problem of tillage is that by creating a good seedbed for the seeds, it creates the same conditions for the weeds. While weed seeds are buried deeply with the mouldboard plough, the same plough brings to the surface the weed seeds that had been buried the season before. The seed bank in most agricultural soils is probably large enough that the plough does not have a long lasting control effect on weeds which multiply by seeds. On the other side, weeds propagating through sprouts or roots can even be multiplied by tillage implements, which only cut and mix them with the soil, so that the number of potential weed plants is increased. Through soil carried with tillage implements from one field to another, the weed population is also spread throughout the entire farmland.

Therefore, the use of tillage for weed control is not the ultimate answer, nor is the move to no-till the ultimate doom in terms of weed control.

How can herbicide use be  reduced?

This brings us back to herbicides. In all farming situations, not only in no-till farming, the use of herbicides can be reduced by applying the products correctly, using the right equipment with the appropriate settings under optimal conditions. Often the application of herbicide is done with even less care than the application of other pesticides, as herbicides are usually considered less toxic than, for example, insecticides. It leads then to increased application rates as the product is not reaching the target, but is wasted in the environment. This can become a problem, where herbicides have not been used traditionally and where, therefore, there is no appropriate equipment available for the application of herbicides once more intensive farming systems are introduced. For example, in the case of Uzbekistan, farmers start using the existing air blast sprayers, which are traditionally used for application of defoliants in cotton, for herbicide application. Similar cases can be found in other

Central Asian countries, such as Mongolia or Kazakhstan, where frequent cultivation of black fallow has been the only weed management strategy for the past few years and where the spray rigs are sometimes in very bad conditions. In FAO projects carried out in these countries, the simple upgrade of existing sprayers with upgrade kits, comprising pumps, controls, hoses and nozzles, reduced the herbicide use compared to farmers practice before the upgrade by 10 to 15 % while the weed control efficiency was at the same time improved by 20 % to values above 90 % control.

What are alternatives for weed management under no-till?

However, the main question remains, whether there are any alternative strategies for weed control that are applicable in no-till farming systems and which would allow reducing the dependency on herbicides. There is actually a wide range of options and principles within a weed management strategy that allow managing weeds without tillage and herbicides.

This starts with a forward looking strategy of weed control, to avoid the maturation and seeding of weeds in the first place by not allowing weed growth even in the off season. Applying this strategy, the farmers in an FAO project in Kazakhstan noticed after only two years of no-till cropping without even using a diversified crop rotation that the weed pressure and, hence, the need for herbicide use was being reduced compared to the conventional tillage based systems.

Another general point is to determine, at which point weeds are actually damaging the crop. It is often not necessary to eradicate the weeds completely, but only to avoid the setting of seeds and competition with the crop. Leaving weeds in a crop at a stage where the crop can suppress them and where there is no damage or problem for the harvest can actually help with managing other pests, such as termites or ants, which in absence of weeds would damage the crop.

A second aspect comes from the soil tillage itself. Farmers who do no-till for several years will notice that weed germination is reduced where the soil is not touched. Once the superficial weed-seed bank is depleted and no new seeds are added, the other seeds still remaining in the soil will not germinate as they will not receive the light stimulus for germination. For this reason, the no-till planters from Brazil,

for example, where no-till farming is reaching nearly 50 % of the total agricultural area, are designed to avoid any soil movement and to cover the seed slot immediately with mulch to create an “invisible” no-till seeding. This is done to reduce the emergence of weed seeds

The most powerful no-tillage and non-chemical weed control in no-till systems, however, is soil cover and crop rotation. Maintaining the soil covered with an organic mulch or a live crop can allow, under certain conditions, notill farming without using any herbicide. For this purpose, it is important to know the allelopathic effects of cover crops. These effects result from substances in the plants which can suppress other plant growth. Cover crops are crops which can be grown between commercial crops to maintain permanent soil cover. Crop rotations have to be designed in such a way, that the soil is always covered and that the variety of crops in the rotation facilitates the management of weeds. For managing the cover crops, a knife roller is used, which breaks the plants and rolls them down.

Applied at the right time, this tool can actually kill some of the cover crops without need of herbicide and achieve complete weed control throughout the next cropping season, provided the planting is done with minimum soil movement. Applying a knife roller, for example, in a well developed cover crop of black oat (Avena strigosa) at milk stage, will completely kill the cover crop, which on the other side will provide good weed control. In Brazil after a cover crop of black oat, there is usually no additional herbicide applied for the following crop There is a lot of scientific and practical evidence that weed infestation under no-till farming using certain cover crops and diversified crop rotations is declining in the long term, allowing a similar decline in herbicide use. Farmers using these principles of good agricultural practice in no-tillage systems report declining pesticide use in general, which also includes declining herbicide use at a level lower than comparable conventional systems.

Starting no-till farming with the establishment of good cover crops and a forward looking weed management allows the introduction of no-till farming in small holder farms in Africa without any herbicide use at all and with a reduction of manual weeding requirements. Spectacular effects were achieved in an FAO project in Swaziland using no chemical inputs and increasing both yields and reducing the drudgery of farm work by introducing a no-till farming system combined with permanent soil cover and crop rotation better known as Conservation Agriculture.


There is no question that herbicide use in agriculture and particularly in no-till farming systems can be a problem. There is plenty of scientific and practical evidence of excessive herbicide use in no-till farming. However, this is not an inherent characteristic of no-tillage farming, as there are alternative ways for weed management even without returning to soil tillage and cultivation. If correctly applied, these practices allow a sustainable use of herbicides in an integrated weed management programme  and even completely non-chemical weed control is possible. These practices are already successfully applied in commercial farming, but globally they are not yet sufficiently known or appreciated. Therefore, the general perception remains that no-tillage farming requires increased herbicide rates, which in reality not true as a general statement.

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