Crop responses to climatic variation

Crop responses to climatic variability and extremes

The yield productivity of many crops has risen over the past 40 years. Rates of yield increase in Europe have ranged from 0.8% (oats; Avena sativa) to 2.6% (triticale; Triticosecale) per year (Ewert et al. 2005). Rates of increase in wheat yields differ between European countries, but regional variation about the linear trend is less clear. More southerly European countries have lower rates of wheat yield increase than more northern ones, suggesting that weather factors such as temperature and precipitation play a more determining role in yield than in the north.. The fact that there have been lower rates of increase in yields in areas of Europe with more extreme conditions and that deviations from a linear trend have increased, points to the conclusion that warming since the start of the 1990s (Schär et al. 2004) has started to affect European wheat yields.

For crops, both changes in the mean and variability of temperature can affect crop processes, but not necessarily the same processes. Some crop processes, mostly related to growth such as photosynthesis and respiration, show continuous and mainly nonlinear changes in their rates as temperature increases. Rates of development and progression through a crop life cycle more often show linear responses to temperature. Both growth and developmental processes show temperature optima, whereby process rates increase over a range but thereafter flatten and decrease. For example, the light-saturated photosynthesis rate of C3 crops such as wheat and rice is at a maximum for temperatures from about 20–32 °C; total crop respiration, the sum of the growth and maintenance components, shows a steep nonlinear increase for temperatures from 15–40 °C followed by a rapid and nearly linear decline. The threshold developmental responses of crops to temperature are often well defined, changing direction over a narrow temperature range, as will be seen later.

An experimental study of climatic variability and wheat

Physiological responses to temperature changes in plants may occur at short or long time-scales (Wollenweber et al. 2003). Rapid changes in enzymatic reactions caused by differential thermosensitivity of various enzymes can deplete or result in accumulation of key metabolites. In addition, short-term effects involving altered gene expression, such as heat-shock protein synthesis, are likely to occur. Longer-term responses include alterations in the rate of carbon dioxide (CO2) assimilation and electron transport per unit leaf area, and impaired cell anaplerotic carbon metabolism, sucrose synthesis and carbon (C) and nitrogen (N) partitioning within and between organs (Jagtap et al. 1998). Altered carbon availability brought about by these events will affect uptake, transport and assimilation of other nutrients, disturb lipid metabolism and injure cell membranes (Maheswari et al. 1999), resulting in changes in growth rates and grain yield (Al Khatib & Paulsen 1999). However, temperature responses for specific physiological processes do not always relate directly to growth, because the latter is an integration of the effects of temperature on total metabolism (Bowes 1991).

The developmental stage of the crop exposed to increased temperatures has an important effect on the damage experienced by the plant (Slafer & Rawson 1995), but experimental studies of the effects of temperature variability on crop productivity are rare. This is mainly because of the difficulties in establishing and maintaining a temperature regime where a mean climatic value can be held constant between treatments that vary the amplitude of temperature (Moot et al. 1996). A solution to this is to examine the effects of extreme conditions at particular developmental stages (Ferris et al. 1998), in which the extreme conditions are defined with reference to literature (Porter & Gawith 1999). Wollenweber et al. (2003) tested the null hypothesis that wheat plants react to two separate periods of high temperature as if they were independent of each other. The chosen stages were the double-ridge stage of the apical meristem, which is close in time to the transition from vegetative to reproductive development of the apical meristem, and anthesis when extreme temperature events interfere with the development of fertile grains, as meiosis and pollen growth are affected (Wallwork et al. 1998). The experimental design, and the extreme temperature conditions were defined as a heat period of eight days of 25 °C at the double-ridge stage and/or a heat event of 35 °C at anthesis. Biomass accumulation, photosynthesis and the components of grain yield were analysed. While a high temperature event of 25 °C at the double-ridge stage is not a stress event sensu strictu for wheat, reproductive spikelet initiation can be impaired (Porter & Gawith 1999) and 25 °C is 13 °C higher than mean daily temperatures measured over 30 years at the experimental site in Denmark.

Grain yields were significantly lower in the treatments with high temperatures at anthesis and at both developmental stages. The major yield component reduced by the treatments was the harvest index; that is, the proportion of total dry matter invested in grain. The harvest index was lower in plants experiencing heat periods because their grain number per plant was reduced by 60% . However, there was no significant difference in the grain yield of plants as between those warmed at anthesis and those at double ridges and anthesis, meaning that the plants experienced the warming periods as independent and that critical temperatures of 35 °C for a short-period around anthesis had severe yield reducing effects. The conclusions from such results for climate change are that yield damaging weather signals for cereals such as wheat are in the form of absolute temperature thresholds, are linked to particular developmental stages and can be effective over short time-periods. This means that yield damage estimates of coupled crop–climate models need to have a maximum temporal resolution of a few days and incorporate models of crop phenology to deal with the overlap between such extreme weather events and crop sensitivity to them.

In contrast to the effects on developmentally linked processes, no significant differences were seen in the relation between light-saturated photosynthesis and leaf internal CO2 concentration for the heat treatments. A heat episode during DR increased the rates of light-saturated photosynthesis (Asat) in green leaves slightly. There were no significant differences in Asat and carboxylation efficiency, reinforcing the conclusion that the principal effects of high temperatures are on developmental processes, such as flowering and the formation of sinks for assimilated carbon, which in itself either is stimulated or is little affected by short-term warming. An extreme heat episode during vegetative development does not seem subsequently to affect the growth and developmental response of wheat to a second heat event at anthesis, and high-temperature episodes seem to operate independently of each other.

Crop temperature thresholds

In addition to the linear and nonlinear responses of crop growth and development processes described above, short-term extreme temperatures can have large yield-reducing effects on major crops. These effects were reviewed for wheat by Porter & Gawith (1999) and, for annual crops in general, by Wheeler et al. (2000). A general point arising from these reviews were that temperature thresholds are well defined as absolute threshold temperatures above which particularly the formation of reproductive sinks, such as seeds and fruits, are adversely affected, as seen in the experiment described above.

The largest standard error found was 5.0 °C for the maximum temperature for root growth, followed by 3.7 °C for the optimum temperature of root growth. Others, such as the base and optimum temperatures for shoot growth, the optimum temperature for leaf initiation and base temperature for anthesis have standard errors of less than 0.5 °C. Thus, the consensus is that functionally important temperatures for wheat are conservative when compared between different studies.

A crop that is important in the developing world is groundnut (Arachis hypogaea L.). This is an important food crop of the semi-arid tropics, including Africa, and can experience temperatures above 40 °C for periods during the growing season (Vara Prasad et al. 2000). The harvestable seeds of groundnut are formed following flowering and fruiting periods. When exposed for short-periods at high temperatures of up to 42 °C just after flowering, a clear relationship between fruit set and mean floral temperature was found (Vara Prasad et al. 2000). From between 32 and 36 °C and up to 42 °C, the percentage fruit set fell from 50% of flowers to zero and the decline in rate was linear, illustrating once more the sharpness of response of crop plants to temperatures between 30 and 35 °C during the flowering and fruiting periods.

Various literature sources have identified similar patterns for other important food crops such as maize and rice. For example, maize exhibits reduced pollen viability for temperatures above 36 °C; rice grain sterility is brought on by temperatures in the mid-30s °C and similar temperatures can lead to a reverse of the vernalizing effects of cold temperatures in wheat. What is perhaps more surprising than the consistent damaging effects of high temperatures in food crops is that cold-blooded animals also exhibit threshold temperature responses for various activities. As with plants, the lethal limits are the widest, followed by activity limits, development and growth with the reproductive limits being the narrowest from 24 to 30 °C, the upper value interestingly close to the limits seen for many crop plants, but this is presumably a coincidence. It would be very useful to have equivalent diagrams for the major crop plants in the world and thereby provide specific quantitative information on the probability and consequences, in other words the risk, from crop damaging climate change at the regional or country level. This would further be the linkage between crop physiology, crop agronomy and climate science (Porter 2005).

(Source – http://rstb.royalsocietypublishing.org/content/360/1463/2021.full)

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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.

(Source –  http://www.agriculturejournals.cz/publicFiles/01158.pdf)

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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.

VEHICLE MODELING

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.

CLOSED-LOOP HEADING RESULTS

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.

CLOSED-LOOP LINE TRACKING RESULTS

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.

CONCLUSION

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.

(Source – http://users.soe.ucsc.edu/~elkaim/Documents/auto_steer_farm_mlo96.pdf)

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Open System for Organic Agriculture Administration

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

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

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

 INTRODUCTION

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

− the market, (promotion and distribution)

− the role of public support and,

− the standards of organic farming.

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

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

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

BASICS OF ORGANIC AGRICULTURE

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

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

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

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

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

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

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

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

− Availability of organic fertilization source from neighbored agricultural exploitations,

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

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

GIS CONCEPTS FOR PRECISION ORGANIC FARMING

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

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

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

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

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

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

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

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

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

ESTABLISHING A FUNCTIONAL POA MODEL

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

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

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

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

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

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

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

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

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

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

COMBINING INTERNET AND GIS

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

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

− Distributed computing power throughout many nodes of the network.

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

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

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

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

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

− GIS users are already familiar by using software menus.

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

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

− It enables tutorials modules and access on educational articles.

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

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

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

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

CERTIFICATIONS AND STANDARDS OF ORGANIC PRODUCTS

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

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

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

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

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

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

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

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

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

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

CONCLUSIONS

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

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

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

(Source – http://www.fig.net/pub/athens/papers/ts20/ts20_5_ifadis_et_al.pdf)

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

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

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

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

Data

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

2. MODIS LAI

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

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

3. AVHRR LAI

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

4. GIMMS NPP

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

5. Crop production

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

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

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

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Satellite mapping

The Need for Better Information

Many remote areas of the World are now being opened to exploration and development, generating a growing demand for up-to-date maps of land topography, land-use and other information products. Such maps are of great importance for activities ranging from managing and planning land-use development, to natural-resource management and engineering studies. In this context, more and more users have started to integrate cartographic and thematic mapping products into their management and decision-making systems.

The mapping industry worldwide is currently experiencing rapid technologicaland organisational change. A prerequisite for obtaining a complete knowledge of any landscape is to rely on as many data sources as possible. Today’s mapping projects are therefore relying more and more on multi-source remote-sensing techniques capable of providing highresolution data sets over wide geographical areas. In addition, map products available in digital format are of a great value in the rapidly expanding market of Geographical Information Systems (GISs). These are now used extensively to integrate data from different sources in domains like land management, monitoring and planning.

Moreover, map producers are looking to the latest and least time-consuming methods to ensure the best product quality at the lowest cost.

The Limitations of Traditional Techniques

Aerial surveys and ground measurements are the conventional means of producing maps. These traditional map-making techniques rely on scanning and digitising processes for their updating and injection into modern decision management systems. However this type of process does not allow the fast and cheap production of maps covering large geographical areas such as entire regions.

Consequently, there is a lack of regular information updating and many maps quickly become obsolete and are therefore of little practical use. information updating and many maps quickly become obsolete and are therefore of little practical use.  Furthermore, statistics provided in 1987 by a United Nations Secretariat Survey showed that more than 40% of the World’s land surface is still not covered by 1:100 000 scale maps. Almost 50% is not covered by 1:50 000 scale maps, and 80% is not covered by 1:25 000 scale maps.

The Benefits of Space-Based Monitoring

Because of its ability to provide fast,  up-to-date information and wide spatial coverage, spaceborn imagery is being used more and more by the mapping industry. Optical remote-sensing data products are used to produce space maps on a wide range of scales to serve many different needs. These include 1:50 000 scale (one of the standard scales) map compilation and map updating, as well as Digital Terrain Models (DTMs) based on stereoscopic imagery. Space maps are geocoded products, annotated in the same way as traditional maps.

Although optical remote-sensing  information is widely used particularly in remote regions, it has two major limitations:

• excessive cloud cover often precludes  its utilisation, and

• the location accuracy of individual  points within a space map is sometimes not sufficient for some applications.

The Contribution of ERS SAR

The Synthetic Aperture Radar (SAR) instrument carried by the European Remote Sensing satellites ERS-1 and  ERS-2 has proved extremely valuable in developing markets dependent on largecoverage maps on scales ranging from 1:1 000 000 to 1:50 000. A large archive of SAR data has been constructed since the launches of ERS-1 and ERS-2 in July 1991 and April 1995, respectively, and this database is continually being updated with new acquisitions.

The SAR is an active instrument that produces images under all weather conditions by analysing the echoes (Cband and VV polarisation) transmitted from the satellite and backscattered by the Earth’s surface. An ERS SAR scene covers an area of 100 km by 100 km and has a high geographical location accuracy. Because of the specific interaction between the radar wave and the ground surface, the information content of SAR images is different from that of optical images.

Ground visibility is much improved in the latter thanks to the radar’s cloud penetration, while the topographic features stand out clearly as a result of the SAR’s oblique viewing angle. Both images have been post-processed and geo-referenced by CEGN (Cellule d’Etudes en Geographie Numerique), the geographic research department of the French armament agency.

The synergy between ERS SAR and optical images, for both cartographic map generation and updating, increases the ability to extract thematic information. Thematic and topographic maps compiled using ERS SAR data allow one to detect and identify specific features such as hydrographic networks and structures, which are particularly important in geomorphologic analysis and geology.

In regions such as the tropics, where optical satellite information is either unavailable or not usable due to excessive cloud cover, space maps generated from SAR data are a precious tool, and sometimes represent the only solution. SAR images can be a unique source of information for compiling highresolution space imagery at a continental scale, and for providing highly valuable thematic information as the sensor can also discriminate between a wide variety of land-cover types.

The capabilities of ERS for large-coverage image mosaicing and thematic mapping worldwide are well-established. They are based on state-of-the-art processing techniques which allow improvement of both the radiometric and geometric quality of the data. For example, the speckle induces alterations in the radiometric resolution of SAR data. This effect, which is inherent in the SAR system and due to the coherent nature of the SAR signal, gives a noisy effect in the images .

Filtering techniques applied on the SAR image can reduce this noisy effect, and thereby enhance image quality. The example shows how multi-temporal filtering techniques have been applied to SAR data by the French company SERTIT, specialised in image processing and GIS systems, in order to reduce the speckle and thus tobe able to use enhanced radarimagery as an input to Geografical Information System. The information content of ERS temporally filtered imageis highly valuble in this context and facilitates the interpretation.

ERS SAR image maps can be exploited for various types of applications:

• cartography: for map compiling and updating taking advantage of the  thematic information provided by the  radar for broad applications and its  capabilities to extract topographic  information.

• localisation: to detect or identify control points and localise targets on the  ground surface, as a complement to  GPS measurements for remote areas.

• rectification: to rectify old or inaccurate  maps, space maps generated using  remote-sensing data with inaccurate  localisation accuracy, or even to rectify a Digital Terrain Model (DTM).

In addition, ERS SAR data products can be used for topographic mapping as Digital Terrain Models can be provided using techniques such as radargrammetry and interferometry (INSAR). Radargrammetry allows one to generate DTMs using stereopairs of radar images with different viewing angles, as for optical stereo  imaging.

INSAR is based on the combination of two ERS SAR images acquired with slightly different geometrical configurations. Using INSAR, highly accurate DTMs can be produced, depending mainly on the stability of the observed surface over time with respect to radar signal phase and atmospheric effects that may affect the phase information during the acquisitions.

Under favourable conditions, the altimetric accuracy achievable can be a few metres.

Differential interferometry allows one to measure surface movements with a sensitivity of the order of a few centimetres over large surfaces. This technique can be applied for subsidence monitoring and in the observation of active volcanoes, earthquakes and faults.

Moreover, interferometry can be used to extract thematic information from ERS SAR data for land-use mapping. By computing the correlation of the SAR signal between the two images, valuable thematic information is obtained. The interferometric correlation, or coherence, mainly depends on radar wave interactions with the target, and its temporal stability. Such derived imagery is used as an additional channel allowing the generation of multi-band radar products.

(Source – http://www.esa.int/esapub/br/br128/br128_2.pdf )

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How to avoid Pesticide Spray Drift

Pesticide spray drift is the physical movement of spray drops from the intended target to any non-target territory and objects. Drift is not just about crop detriment; it can negatively impact farm workers, organic crops, the general public, beehives, gardens, aquatic areas and other sensitive habitats, even if the effects are not immediate or obvious.

Pesticide labels vary with regard to information on spray drift management. Some labels give a detailed list of required drift management techniques. Labels may specify a maximum wind speed in which to spray, or simply indicate not to apply under windy conditions. Labels may also require an “adequate” or specific size buffer zone between the target site and sensitive sites, such as areas occupied by humans, animals or susceptible vegetation.

The discussions continue on how prevent spray drift. A growing number of registries in certain states enable applicators to determine the location of sensitive crops in close proximity to their planned treatments. Application technology and buffer size calculations are also becoming more complicated, but ultimately it is the applicator who must take every necessary precaution.

There is no one technique that can minimize spray drift. The applicator must consider the non-target sites downwind of the application, location of buffers, weather conditions and application equipment. Follow all government regulations and label directions and carefully evaluate the following:

Non-Target Sites. Know what is downwind of your application – not only on your land, but on neighboring land as well. A small amount of spray drift to a tolerant, labeled crop on your land is very different than drift to a sensitive crop or to anything on someone else’s ownership. If possible, make the application when the wind is blowing away from any non-target site of concern.

Buffers. Establish buffers, which are areas or strips of land intended to intercept spray drift. At times, a specific buffer size will be required by the Environmental Protection Agency (EPA) when it approves the label; in other instances, the need for buffers will be assessed by the applicator based on professional judgment and local conditions. Tolerant fast-growing trees, grassed buffer strips and non-performing field borders are examples of buffers that can be positioned downwind of areas that will be treated. Know the effectiveness of the buffer as well. For example, a tall, continuous buffer of tolerant trees will provide much better protection from drift than a narrow strip of low-growing grass.

When no buffer exists (or an existing buffer is insufficient under the particular application conditions), create the needed buffer by leaving a portion of the target site untreated. The size and location of this “flexible” buffer is determined on an application-by-application basis by considering all the factors influencing spray drift potential.

Weather. Wind is the most influential weather factor affecting spray drift. Apply pesticides only when winds are light and blowing away from sensitive areas. A general rule is to spray when the wind speed is 3-10 mph, but the upper limit must be modified based on all application-specific factors influencing drift. Accurately measure wind speed and direction before and during the application.  If a change in wind speed or direction results in unacceptable drift, immediately adjust the buffer size or location as necessary, or stop the application.

Calm conditions or variable winds can actually increase the chance of spray drift. Calm conditions might indicate the presence of a temperature inversion (a trapped layer of air). Inversions, which are most common during the early morning or evening, favor horizontal movement of pesticides.

High temperatures and low relative humidity during the application may also increase the chance of spray drift by increasing evaporation, which reduces the size of spray droplets. Keep accurate records of wind speed and direction, air temperature and relative humidity.

Application Equipment. Spray pressure and volume, droplet size, nozzle type, boom height and additives can all influence spray drift. Within the constraints of the label:

  • Reduce spray pressure to produce larger spray droplets, which are less likely to drift.
  • Increase spray volume, which allows the use of nozzles that produce larger droplets.
  • Use low-drift nozzles, such as those with air-induction technology.  Replace all worn nozzles.
  • Keep the spray boom as low as possible without detrimentally affecting spray coverage. Consider boom shields and windscreens.
  • Include a drift control agent in the spray tank.

Some of these spray drift-reducing tactics cannot be used for every pesticide application because pest control will be reduced. But, if you cannot follow the label AND avoid drift, select a different product or formulation. Granules (such as weed-and-feed products) are sometimes available alternatives to the use of liquid sprays to eliminate drift.

(Source  – http://www.striptillfarmer.com/pages/News—Tips-To-Avoid-Pesticide-Spray-Drift.php)

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Vegetation Control: Profitable Investment

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

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

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

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

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

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

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

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

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

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

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

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Weather History

To get data on historical temperature figures, cloudiness and humidity indices for each month in different regions or countries all over the world you can use World Meteorological Organization database  by following this link.

Moreover, some crop monitoring systems (e.g. satellite vegetation monitoring systems) offer the option of precise weather forecast backed by historical database

Tags: World Meteorological Organization, forecast, weather, agriculture, temperature, cloudiness, humidity, crop, yield

 

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Precision Agriculture and the Ukrainian Reality

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

Ukrainian-type Efficiency of Agricultural Business

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

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

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

Let’s Change the Principles!

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

Precision Agriculture

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

Monitoring Systems

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

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

Cost Savings Plain to See

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

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

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

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

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

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