DOES NO-TILL FARMING REQUIRE MORE HERBICIDES?

“More” compared with…?

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

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

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

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

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

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

What is the weed control effect  of tillage?

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

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

How can herbicide use be  reduced?

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

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

What are alternatives for weed management under no-till?

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

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

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

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

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

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

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

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

Conclusions

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

(Source – http://www.fao.org/ag/ca/CA-Publications/Pesticide%20Outlook%202005.pdf)

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Comparison of the effect of liquid humic fertilizers

Maize (Zea mays L.) is one of the most highly consumed  crops, and the most important foodstuff after wheat and  rice around the world. The global production of maize is 604 million tons, with a planting area of up to 140 million hectares. Iran produces 2 million tons of maize on 350000 hectares of land. However, the production from hybrid maize seeds in Iran is highly limited (FAO, 2002).

This plant, photosynthetically, is of C4 type and thrives in tropical and semitropical climates (Emam, 2008) and is native for central and southern America (Khodabandeh, 1998). Based on its role in production of grain and forage and providing food for livestock, as well as its industrial use, maize has become an important crop in Iran, as well as in other parts of the world. Expanding the area under  maize cultivation in Iran in order to become self-sufficient is one the most important goal pursued by the government and as a result of implementing programs designed to increase grain maize production over the last few years, this crop has seen a very fast growth in terms of planting area and yield.

Humic substances (HS) are the result of organic decomposition of the natural organic compounds comprising 50 to 90% of the organic matter of peat, lignites, sapropels, as well as of the non-living organic matter of soil and water ecosystems. Authors believe that humic substances can be useful for living creatures in developing organisms (as substrate material or food source, or by enzyme-like activity); as carrier of nutrition; as catalysts of biochemical reactions; and in antioxidant activity (Kulikova et al., 2005). Yang et al. (2004) argued that humic substances can both directly and indirectly

affect the physiological processes of plant growth. Soil organic matter is one of the important indices of soil fertility, since it interacts with many other components of the soil. Soil organic matter is a key component of land ecosystems and it is associated with the basic ecosystem processes for yield and structure(Pizzeghello et al., 2001).

Classically, humic substances are defined as a general group of heterogeneous organic materials which occur naturally and are characterized by yellow through dark colors with high molecular weight (Kulikova et al., 2005).  Shahryari et al. (2011) experienced the effect of two types of humic fertilizers (peat and leonardite derived) on germination and seedling growth of maize genotypes. They reported that interaction of “genotype × solutions (peat and leonardite based humic fertilizers and control) was significant in terms of the length of primary roots.

Application of leonardite based humic fertilizer had a remarkably more effect on relative root growth of Single Cross 794 and ZP 434 than other genotypes. In their experiment, the relation between germination rate and primary roots was positively significant under the condition of application of both types of humic fertilizers; but there was not the same relation for control treatment.

They argued that all types of various humic substances as a biological fertilizer can have an either similar or different effect in early growth stages of maize, as peat and leonardite based fertilizers that they applied produced more seedling roots than control, however the length of coleoptiles was higher in treatment with application of leonardite based humic fertilizer and control than treatment with application of peat based humic fertilizer. They believe that if used in lower quantity these natural fertilizers can have a lot of effect on plant growth.

Hence, in order to recognize how effective they might be, investigations should be considered based on various amounts of humic fertilizers. Finally, they suggested that both peat and leonardite based humic fertilizers could be used to stimulate growth of primary roots in maize which are critical for an optimal establishment of maize in the field.

Gadimov et al. (2009) claimed that humic substances are natural technological products with a miraculous biological effect on crops and concluded that a scientific and practical program is required to make use of this technology in the world, particularly in developing countries. Also, Shahryari et al. (2009) concluded that potassium humate is a miraculous natural material for increasing both quantity and quality of wheat and can be used to produce organic wheat. Thus, application of biological products such as humic fertilizers to provide nutrition for crops can be one of the useful methods to achieve some of the objects of organic crop production.

In addition, Shahryari et al. (2011) studied the response of various maize genotypes against chlorophyll content of the leaves at the presence of the two types of humic fertilizers. In their experiment, solutions (two types of peat and leonardite based liquid humic fertilizers and control) and interaction of “genotypes × solutions” produced significant difference at 1% probability level in terms of chlorophyll content of the leaves. Genotypes such as Single Cross 704 and 505 had the highest index for chlorophyll content when treated by leonardite based humic fertilizer. Peat based humic fertilizer decreased the index for chlorophyll content in genotypes such as 500, OS499 and 505, while leonardite based humic fertilizer decreased the index for chlorophyll content of the leaves in genotypes such as Golden West and Single Cross 704. However, peat based humic fertilizer did not have such an effect on these two maize genotypes.

Meanwhile, leonardite based humic fertilizer had no effect on index for chlorophyll content of leaves in genotypes such as 500, OS499 and 505. Genotypes such as ZP677 and ZP434 produced no response against the application of the two types of humic fertilizers. This study was aimed to compare the effect of liquid peat and leonardite based humic fertilizers on the yield of maize genotypes in Ardabil Region.

MATERIALS AND METHODS

This experiment was conducted at Agriculture Research Station of Islamic Azad University, Ardabil Branch (5 km west of Ardabil City) in 2009 – 2010 cropping year. The region has a semiarid and cold climate, where the temperature during winter season usually drops below zero. This region is located 1350 m above the sea level with longitude and latitude being 48.2°E and 38.15°N, respectively.

Average annual minimum and maximum temperatures are -1.98and 15.18°C, respectively; whereas maximum absolute temperature is 21.8°C; and mean annual precipitation has been reported to be 310.9 mm. The soil of the field was alluvial clay with a pH ranging from 7.8 to 8.2.

Vegetative materials included six maize genotypes prepared from the Agriculture and Natural Resources Research Center of Ardabil Province. The Experiment was conducted as split plot in the basisof randomized complete block design with three replications. The main factor included three conditions (peat based humic fertilizer; leonardite based humic fertilizer; without the application of humic fertilizer) and the sub factor included six maize genotypes (ZP677, Golden west, OS499, ZP434, Ns540 and Single Cross 704). Each of experimental blocks included 3 plots, 320 cm length in rows, with80 cm from each other containing plants at 20 cm distances.

Pretreatment of seeds were done on the basis of 220 ml per 10 L of water to be applied for 1 ton of seeds. Moreover, irrigation was done in flooding manner. Weed-fighting was done both mechanically and manually during all growth stages. Liquid humic fertilizer was prepared and applied based on 400 ml per 50 L of water for 1 ha of maize plantation. The prepared solution was sprayed upon the aerial part of the plants during 5th leaf stage, appearance of reproductive organs, flowering and grain filling stages. All the samples were taken randomly from competitive plants at middle rows. Study traits included grain number per ear row, number of grain row per ear, ear number, weight of 1000 grains, biological yield, vegetative yield and grain yield.

Statistical analysis

Analysis of variance of data and mean comparison of them was done using MSTATC and SPSS programs. Mean comparison was done using Duncan’s multiple range test, at 5% probability level. Due to abnormality of data for ear number and its high coefficient of variation, square root conversion was used to normalize it.

RESULTS AND DISCUSSION

Results from analysis of variance for study traits suggest that there was a significant difference  between experimental conditions in terms of grain yield and biological yield at 1 and 5% probability levels, respectively. In addition, there was a nonsignificant difference between study genotypes in terms of all evaluated traits except for number of grain row per ear and wet biomass at 1% probability level. Furthermore, there was no difference observed between the interaction of genotype and experimental conditions for any trait being studied, and this was in agreement with the report of Shahryari et al. (2009). This means that under study genotypes had the same responses to potassium humate.

Moreover, results from mean comparison of data (Table 2) for studied genotypes indicate that genotype OS499 (110.70 g) had the highest 1000 grain weight, whereas genotype Single Cross (81.20 g) had the lowest 1000 grain weight on average. Based on mean comparison of 1000 grain weight, genotypes OS499 and ZP434 were placed in the same group as NS540, whereas genotype ZP677 was placed in the same group as Golden West. Genotype ZP677 (with a mean value of 15.48) and genotype ZP434 (with a mean value of 13.49) had the highest and lowest values of number per ear, respectively; and genotypes such as Golden West and Single Cross were placed in  the same group as NS540 and had no difference in terms of this trait. Genotype ZP677 (with a mean value of 20.89 ton/ha) and genotype OS499 (with a mean value of 16.93 ton/ha) had the highest and lowest biological yield respectively and genotype OS499 was placed in the same group as ZP434, whereas genotypes such as Golden West and Single Cross were placed in the same group as NS540. Genotype ZP677 (with a mean value of 108.68 ton/ha) was the best among other genotypes in terms of wet biomass, whereas ZP434 (with a mean value of 77.52 ton/ha) had the lowest value for wet biomass. ZP677 was placed in the same group as NS540, whereas genotypes Golden West and OS499 were placed in the same group as ZP434 and had no difference in terms of this trait.

Shahryari and Shamsi (2009a) reported that potassium humate increased the rate of biological yield of wheat from 3.26 to 3.72 g/plant; but it had no effect on harvest index. Also, they found that uses of potassium humate increased grain yield. Results from mean comparison of data  for experimental conditions being studied indicate that application of leonardite based liquid humic fertilizer produced the highest biological yield(21.99 ton/ha on average), whereas no application of humic fertilizer produced the lowest biological yield(14.97 ton/ha on average). In this respect, both types of applied humic fertilizers had similar effects. Application of leonardite based liquid humic fertilizer produced the highest grain yield (7.09 ton/ha on average) among the conditions being studied, whereas under the condition of without humic fertilizer obtained the lowest value(4.07 ton/ha).

Ayas and Gulser (2005) reported that humic acid leads to increased growth and height and subsequently increased biological yield through increasing nitrogen content of the plant. It has also been reported that application of humic acid in nutritional solution led to increased content ofnitrogen within aerial parts and growth of shoots and root of maize (Tan, 2003). In another investigation, the application of humic acid led to increased phosphorus and nitrogen content of bent grass plant and increased the accumulation of dry materials (Mackowiak et al.,2001). Humic acid leads to increased plant yield through positive physiological effects such as impact on metabolism of plant cells and increasing the

concentration of leaf chlorophyll (Naderi et al., 2002).

Also, spraying humic acid on wheat crop increased its yield by 24% (Delfine et al., 2002). In general, the results from this study indicate that the application of leonardite based humic fertilizer increased biological yield by 46.89% compared to control, whereas peat based humic fertilizer increased biological yield by 34.47% compared to control. Seyedbagheri (2008)evaluated commercial humic acid products derived from lignite and leonardite in different cropping systems from 1990 to 2008. The results of those evaluations differed as a result of the source, concentration, processing, quality, types of soils and cropping systems. Under their research, crop yield increased from a minimum of 9.4%to a maximum of 35.8%. However, application of humic fertilizer in this study increased the biological yield by 40.68% on average. Application of leonardite based humic fertilizer increased the grain yield of maize by 74%.

Also, peat based humic fertilizer increased the grain yield by 44.7%. Overall, the mean increase for grain yield under the condition of application of humic fertilizers was as high as 59.45%. Similar results were also presented by Shahryari et al. (2009b) on wheat. They reported increase of grain yield (by 45%) from 2.49 ton/ha to 3.61 ton/ha affected potassium humate derived from sapropel in normal irrigation conditions.

Conclusion

Results from this experiment indicate that the application of liquid humic fertilizer can positively affect the maize yield and some agronomic traits related to it. These desirable effects can be a consequence of its effect on the physiology of the maize. In general, application of humic acid can lessen the need for chemical fertilizers and subsequently reduce environmental pollution, and compared with other chemical and biological fertilizers, they are affordable. Finally, it can be said that application of humic fertilizer not only increases the yield of maize, but also can play a significant role in achieving the goals of sustainable agriculture

( Source http://www.academicjournals.org/ajb/PDF/pdf2012/13Mar/Khaneghah%20et%20al.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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Agriculture crop management and production was improved by satellite remote sensing technology and geographic information systems (GIS)

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

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

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

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

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

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

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

Normalized Difference Vegetation Index (NDVI)

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

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

Green Vegetation Index – Colorized Map

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

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

Soil Zone Index

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

TreeGrading

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

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

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

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Crop Cultivation Seasonality

You can learn crop growing seasonal peculiarities for different countries and regions by using FAO database by following this link.

Tags: crop, seasonality, FAO, forecast, agriculture

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