Showing papers on "Precision agriculture published in 2006"

Handbook of Precision Agriculture: Principles and Applications

Abstract: * About the Editor * Contributors * Foreword (M S Swaminathan) * Preface * Acknowledgments * PART I: PRINCIPLES, TECHNOLOGIES, AND MANAGEMENT ISSUES * Chapter 1 Precision Agriculture: An Overview (Ancha Srinivasan) * Introduction * Basics of Precision Agriculture * Tools for Implementation of Precision Agriculture * Current Status, Uncertainties, and Future Trends * Epilogue * Chapter 2 The Role of Technology in the Emergence and Current Status of Precision Agriculture (John V Stafford) * The Beginnings of Precision Agriculture * The Basis for Precision Agriculture: Information Technology * Spatial Location * Basics of GPS * Information Acquisition: Sensors * Crop Condition * Weed Detection * Grain Yield * Grain Quality * Environment * Assembling and Interpreting Information * Utilizing Information: Application and Control * Agrochemicals * Patch Spraying: Philosophy of Approach * Fertilizers * The Role of Precision Agriculture in the Future of Agriculture-Technological Developments * Chapter 3 Soil Sensors for Precision Farming (Sakae Shibusawa) * Introduction * Current Developments and Use of Soil Sensors * Future Development and Prospects * Conclusions * Chapter 4 Site-Specific Nutrient Management: Objectives, Current Status, and Future Research Needs (Silvia Haneklaus and Ewald Schnug) * Introduction * Origins of SSNM * Data Sources for SSNM * Decision Making for SSNM * SSNM for Different Nutrient Sources * Interaction of SSNM with Other PA Measures in the Field * Quality Aspects * Economic, Ecological, and Social Impacts of SSNM * Future Research Needs * Chapter 5 Precision Water Management: Current Realities, Possibilities, and Trends (Carl R Camp, E John Sadler, and Robert G Evans) * Introduction * Current Status * Irrigation Application and System Control * Auxiliary System Components * Management Zones * Applications and Justifications * Current Trends * Cost-Benefit Issues * Future Directions * Conclusions * Chapter 6 Site-Specific Weed Management (Roland Gerhards and Svend Christensen) * Introduction * Weed Distribution in the Field * Stability of Weed Populations * Weed Monitoring * Decision Making * Site-Specific Herbicide Application * Site-Specific Weed Control * Future Directions * Chapter 7 Site-Specific Management of Crop Diseases (Karsten D Bjerre, Lise N Jorgensen, and Jorgen E Olesen) * Introduction * The Disease Management Arena * IPM Strategies for Disease Control * Site-Specific Disease Control: The Next Step in the Evolution of Disease Management * Effects of Diseases and Spatial Variability on Crop Growth * Technology for Site-Specific Disease Management * Perspectives * Chapter 8 Site-Specific Management of Plant-Parasitic Nematodes (Robert A Dunn, Jimmy R Rich, and Richard E Baird) * Introduction * General Nematode Biology * Diagnosing Nematode Problems * Principles of Nematode Management--Nonchemical * Nematicides * Variable-Rate Nematicide Application * Chapter 9 Site-Specific Measurement and Management of Grain Quality (Piet Reyns, Josse De Baerdemaeker, Ludo Vanongeval, and Maarten Geypens) * Introduction * Quality Factors and Their Measurement * On-Line Quality Measurements * Influence of Plant Nutrition on the Quality of Cereal Crops * Grain Quality and Crop Management * Site-Specific Crop Quantity and Quality Management * Conclusions * PART II: APPLICATIONS IN CROPS AND CROPPING SYSTEMS * Chapter 10 Site-Specific Rice Management (Alvaro Roel, G Stuart Pettygrove, and Richard E Plant) * Introduction * Quantifying Spatial Variability and Its Causes * Discussion * Chapter 11 Precision Agriculture Management Progress and Prospects for Corn/Soybean Systems in the Midwestern United States (Thomas S Colvin) * Introduction * Experimentation in Central Iowa * Availability of Yield Monitors and Site-Specific Soil Testing * Other Benefits of Yield Monitors * Status of Soil Sampling * Profitability * Environmental Issues * The Human Side of Precision Agriculture * The Need for Future Research * Chapter 12 Site-Specific Management of Cotton Production in the United States (Richard M Johnson, Judith M Bradow, and Anne F Wrona) * Introduction * Soil Informational Layer * Crop Informational Layer * Remote Sensing Informational Layer * Integration of Informational Layers * Acceptance of Site-Specific Management by Cotton Producers * Chapter 13 Potential of Precision Farming with Potatoes (Colin McKenzie and Shelley A Woods) * Introduction * Nutrient Management * Remote Sensing * Nematodes * Insects * Weed Control * Harvesting and Seeding Equipment * Soil Salinity * Field Scale Experimentation * Problems Hindering the Adoption of Precision Farming by the Potato Industry * Conclusions * Chapter 14 Site-Specific Management in Sugarbeet (David W Franzen) * Properties of Sugarbeet Favorable to Site-Specific Nutrient Management * Zone Management of Nutrients * Profitability of Using Site-Specific Nitrogen Management in Sugarbeet * Use of Imagery from Sugarbeet to Modify Nitrogen Recommendations to Subsequent Crops * Conclusions * Chapter 15 Application of Remote Sensing and Ecosystem Modeling in Vineyard Management (Ramakrishna R Nemani, Lee F Johnson, and Michael A White) * Introduction * The Vineyard As an Ecosystem * Tools in Vineyard Management * Conclusions * Chapter 16 Site-Specific Management from a Cropping System Perspective (David E Clay, Sharon A Clay, and Gregg Carlson) * Introduction * Understanding Yield Variability * Managing Yield Variability * Conclusions * PART III: CURRENT STATUS * Chapter 17 Africa (W T (Wimpie) Nell, Ntsikane Maine, and P M Basson) * Introduction * Climatic Conditions * Background of Agriculture * Site-Specific Management * Precision Agriculture * Constraints in the Adoption of Precision Agriculture and Site-Specific Management Technologies * Research on Precision Agriculture in South Africa * Prospects for Precision Agriculture * Chapter 18 Asia (Ancha Srinivasan) * Introduction * Spatial Variability in Asian Farms * Drivers and Opportunities for Adoption of Precision Farming * Current Status in Selected Countries * Constraints and Approaches for Adoption * Implications for Adoption in Asia * Future Action * Conclusions * Chapter 19 Australia (Simon E Cook, Matthew L Adams, Robert G V Bramley, and Brett M Whelan) * Introduction * What Precision Agriculture Means in Australia * Demand for Precision Agriculture in Australia: The Battle for Sustainability Needs Accurate and Relevant Information * Methods Used in Australia * Applications in the Grains, Cotton, Wine, and Sugar Industries * Impediments to Adoption * Conclusions * Chapter 20 Europe (Simon Blackmore, Hans W Greipentrog, Soren M Pedersen, and Spyros Fountas) * Introduction * The Current Situation in European Farming * Precision Farming Research in Europe * Variability and Management * Technology-Led Opportunities * Issues of Adoption and Farmer Attitudes * Future Research * Conclusions * Chapter 21 Argentina (Rodolfo Bongiovanni and Jess Lowenberg-DeBoer) * Introduction * Argentine Agriculture * Current Status * Factors That Favor Adoption * Factors That Discourage Adoption * Prospects * Challenges * Chapter 22 Brazil (Glaucio Roloff and Daniele Focht) * Introduction * A Brief History of Precision Agriculture in Brazil * Precision Agriculture on Highly Weathered Soils * Managing Variability * Precision Agriculture for Specific Crops * Conclusions * Index * Reference Notes Included

Assessment of cereal nitrogen requirements derived by optical on-the-go sensors on heterogeneous soils

Abstract: Variable N management is one of the most promising practices of precision agriculture to optimize nitrogen-use efficiency (NUE) and decrease environmental impact of agriculture. The objective of this study was to test the performance of fertilization in winter wheat (Triticum aestivum L.) and triticale (Triticosecale Wittm.) determined by reflection measurements of on-the-go sensors under heterogeneous field conditions. In 2004 geo-referenced yield and N fertilization data were collected in four heterogeneous fields in southern Germany. Nitrogen demand of plants was determined throughout the growing season and the corresponding amount of N fertilizer was broadcast with the N-Sensor (Yara, Germany) in real-time. The sensor uses the red edge position (720-740 nm) as an indicator of crop N status and relates this to crop N demand. The sensor algorithm is designed to stimulate plant growth in areas with low biomass and reduce risk of lodging in areas with high biomass. Fertilization was evaluated by calculating site-specific N balance maps to delineate zones with N surplus in the soil. The results revealed some general limitations of this sensor approach in areas with yield-limiting factors other than N. Nitrogen surplus above 50 kg ha -1 was calculated for subfield areas dominated by shallow soils. The results of this study indicated that sensor-based measurements can be used efficiently for variable N application in cereal crops when N is the main growth-limiting factor. However, the causes for variability must be adequately understood before sensor-based variable rate fertilization can safely be used to optimize N side-dressing in cereals.

A wireless sensors network for monitoring environmental variables in a tomato greenhouse

Abstract: Precision Agriculture is based on detailed information on the status of crops: for example, some of the processes like fertilization and especially crop protection require frequent updates in information. Wireless sensors, continuously acquiring data, could play a role in preserving the environment by reducing pesticide usage and maximizing quality. These benefits need to be tested in the field. The Rinnovando group (Rgroup) is working with agricultural experts on a short-term deployment of a wireless sensors network in a tomato greenhouse in the South of Italy. In this project, Sensicast devices are used in order to apply of Wireless Sensor Networks (WSN) in agriculture and particularly that of microclimate monitoring within a greenhouse incorporating sensor nodes in an agricultural ICT infrastructure.

An analysis of the potential of precision farming in Northern Europe

Abstract: . Precision farming is the process of adjusting husbandry practices within a field according to measured spatial variability. In this review, we explore the prospects for precision farming using the principles that underly conventional soil management and agronomy. The cost-effectiveness of precision farming is determined by the cost of defining zones within fields, the stability of zones through time, the difference in treatment between zones in terms of cost, and the responsiveness of the crop in terms of yield and quality to changes in treatment. Cost-effective precision farming is most likely where prior knowledge indicates large heterogeneity and where treatment zones can be predicted, for example from soil type or field history. Soil related factors are likely to provide the main basis for precision farming because they tend to be stable through time and influence crop performance. In particular, soil mapping may usefully indicate the moisture available for crop growth, organic matter maps may be utilized for precision application of fertilizers and soil acting herbicides, and variation in soil pH can be mapped and used as a basis for variable lime application. However, comprehensive nutrient mapping is less likely to be economic with existing techniques of chemical analysis. The value of yield mapping lies in identifying zones which are sufficiently stable to be of use in determining future practices. Maps of grain quality and nutrient content would significantly augment the value of yield maps in guiding marketing decisions and future agronomy. Interactions between soil differences and seasonal weather are large, so yield maps show considerable differences from season to season. Interpretation of such maps needs to follow a careful, informed, analytical process. Extensive and thorough field experimentation by crop scientists over many years has shown that yield variation arises as a result of a large and complex range of factors. It is highly improbable that simple explanations will be appropriate for much in-field yield variation. However, the capacity to sense yield variability within fields as opposed to between fields, where there are many confounding differences, provides an opportunity for the industry to improve its understanding of soil-based effects on crop performance. This should support its decision taking, whether through precision farming or through field-by-field agronomy. The main obstacle to the adoption of precision farming is the lack of appropriate sensors. Optimal sensor configurations that will measure the specific needs identified by end-users need to be developed. The conclusions reached in this paper probably apply to farming throughout northern Europe.

Maximum benefit of a precise nitrogen application system for wheat

Abstract: Research is ongoing to develop sensor-based systems to determine crop nitrogen needs. To be economic and to achieve wide adoption, a sensor-based site-specific application system must be sufficiently efficient to overcome both the cost disadvantage of dry and liquid sources of nitrogen relative to applications before planting of anhydrous ammonia and possible losses if weather prevents applications during the growing season. The objective of this study is to determine the expected maximum benefit of a precision N application system for winter wheat that senses and applies N to the growing crop in the spring relative to a uniform system that applies N before planting. An estimate of the maximum benefit would be useful to provide researchers with an upper bound on the cost of delivering an economically viable precision technology. Sixty five site-years of data from two dryland winter wheat nitrogen fertility experiments at experimental stations in the Southern Plains of the U.S.A. were used to estimate the expected returns from both a conventional uniform rate anhydrous ammonia (NH3) application system before planting and a precise topdressing system to determine the value of the latter. For prices of $0.55 and $0.33 kg−1 N for urea-ammonium nitrate (UAN) and NH3, respectively, the maximum net value of a system of precise sensor-based nitrogen application for winter wheat was about $22–$31 ha−1 depending upon location and assumptions regarding the existence of a plateau. However, for prices of $1.10 and $0.66 kg−1 N for UAN and NH3, respectively, the value was approximately $33 ha−1. The benefit of precise N application is sensitive to both the absolute and relative prices of UAN and NH3.

Field and crop information gathering system

Abstract: A method, apparatus, and system related to field and crop information gathering. In one aspect of the invention, data is obtained from a producer. One example would be yield map data from a yield monitor. Another is “as planted” data from precision farming planting equipment. The data includes information about seed or crop in a producer's field and an identification of the field. The data is combined with other information in a report. The information added could be, for example, soil type information overlaid on the field map. Another example is environmental classification information overlaid on the field map. The report is returned to the producer and used to discuss planning related to the field and the seed or crop.

Potential use of near infrared reflectance monitoring in precision agriculture

Abstract: The concept of Precision farming is not new, and interest in the potential benefits gained momentum in the late eighties. The high cost of soil sampling and chemical and physical analysis by conventional laboratories has restricted the full implementation of this technique at the field level. Near infrared reflectance (NIR) could be a cost‐effective solution. Soil properties that have been calibrated include gravimetric soil water, clay content, buffer capacity, pH, electrical conductivity, titratable acidity, organic matter, mineralizable nitrogen, potential ammonia volatilization from urea, potential nitrification rate, and urease activity. A whole paradigm shift in philosophy is needed in soil testing to move away from the traditional approach of taking a perceived‐representative sample, in which all the spatial variation is lost, to using a combination of grid soil sampling at a sample intensity of 4 to 10 cores per ha and analysed separately using rapid but less accurate methods such as NIR.

Instrumentation and Control for Wireless Sensor Network for Automated Irrigation

Abstract: An in-field sensor-based irrigation system is of benefit to producers in efficient water management. A distributed wireless sensor network eliminates difficulties to wire sensor stations across the field and reduces maintenance cost. Implementing wireless sensor-based irrigation system is challenging on seamless integration of sensing, control, and data communication. An automated sensor-based irrigation system was developed for an integrated wireless in-field sensor network and automated variable rate irrigation. Field conditions were real-time monitored sitespecifically by in-field sensor stations distributed across the field. Each sensor station measured soil moisture, soil temperature, and air temperature, while one weather station recorded precipitation, wind speed and direction, air temperature, relative humidity, and solar radiation. Sensors and a data logger were self-powered by a solar panel and sensory data was periodically sampled and wirelessly transmitted to a base station about 700 m away from the sensor stations. A host computer received and real-time displayed field data without interference. This paper describes details of the design and construction of wireless communication, hardware used, and the costs and benefits of the control system.

Hyperspectral mapping of crop and soils for precision agriculture

Abstract: Precision agriculture requires high spectral and spatial resolution imagery for advanced analyses of crop and soil conditions to increase environmental protection and producers' sustainability. GIS models that anticipate crop responses to nutrients, water, and pesticides require high spatial detail to generate application prescription maps. While the added precision of geo-spatial interpolation to field scouting generates improved zone maps and are an improvement over field-wide applications, it is limited in detail due to expense, and lacks the high precision required for pixel level applications. Multi-spectral imagery gives the spatial detail required, but broad band indexes are not sensitive to many variables in the crop and soil environment. Hyperspectral imagery provides both the spatial detail of airborne imagery and spectral resolution for spectroscopic and narrow band analysis techniques developed over recent decades in the laboratory that will advance precise determination of water and bio-physical properties of crops and soils. For several years, we have conducted remote sensing investigations to improve cotton production through field spectrometer measurements, and plant and soil samples in commercial fields and crop trials. We have developed spectral analyses techniques for plant and soil conditions through determination of crop water status, effectiveness of pre-harvest defoliant applications, and soil characterizations. We present the most promising of these spectroscopic absorption and narrow band index techniques, and their application to airborne hyperspectral imagery in mapping the variability in crops and soils.

Using Spatial Analysis to Study the Values of Variable Rate Technology and Information

Abstract: We present a review of the last few years' literature on the economic feasibility of variable rate technology in agriculture. Much of the research on this topic has involved the estimation of site-specific yield response functions. Data used for such estimations most often inherently lend themselves to spatial analysis. We discuss the different types of spatial analyses that may be appropriate in estimating various types yield response functions. Then, we present a taxonomy for the discussion of the economics of precision agriculture technology and information. We argue that precision agriculture technology and information must be studied together since they are by nature economic complements. We contend that longer-term, multi-location agronomic experiments are needed for the estimation of ex ante optimal variable input rates and the expected profitability of variable rate technology and information gathering. We use our taxonomy to review the literature and its results with consistency and rigor.

Unidades de manejo a partir de dados de produtividade

Abstract: Determination of management zones using yield data. Precision agriculture is a set of technologies that aims the efficiency increase based on the differentiated management of agricultural areas. In this context, it is important to establish methodologies to use the yield information, soil or indicators in the determination of management zones. The aim of this paper was to develop a methodology for the definition of management zones according to yield maps of five growing seasons. The soybean yield was measured from 1998 to 2002, in an area in Cascavel, Parana State, Brazil. Yield maps were generated using geostatic techniques. The sampled area has 1.74 ha with 256 plots: 128 with site- specific chemical management and 128 without site-specific chemical management. A plot combine was used for harvest. In the experiment the spatial dependence was verified for both planting systems in each year. The punctual values of soybean yield of each year were standardized using the standard score technique. After that, these values were reclassified in low, medium and high, allowing the comparison of productivities in different years and the generation of a yield average map. Management zones were generated according to the standardized yields and also to the coefficient of variation being classified in low, medium and high. The methodology was efficient to identify homogeneous zones.

Spectral indices for precise agriculture monitoring

Abstract: This paper presents two main objectives of a multi‐year study applying remote sensing to precision agriculture: (1) developing new spectral indices for wheat monitoring, and (2) producing an interpretation key for mapping vegetation features with spectral indices. Agricultural monitoring with remote sensing utilizes and maps the spectral reflection of specific vegetation features. These are the indicators of plant development and crop condition. Over the years, a number of spectral indices have been developed, but the ultimate combination of information required by the farmer, and the capability of remote sensing to map this information, has not yet been achieved. The study, which lasted three years and was performed simultaneously, collected vegetation and remote‐sensing data. The study aimed to improve the current abilities of remotely sensed agriculture monitoring. Indices were developed relating to various features of wheat. These indices map the current conditions of the crop, such as nitrogen in the...

Crop sensors for efficient nitrogen management in sugarcane: potential and constraints

Abstract: A real-time crop sensor for site specific input application is the new innovation in the field of precision agriculture. At least three different type of crop sensors,viz.. Soil Doctor, N-Senior and Green Seeker have been used in different field crops. The key advantage of all these systems is that they do not need recommendation maps. However, no published data is available on Soil Doctor adoption by farmers due to company’s aggressiveness for protective patent rights. The N-Sensor is being used mainly for wheat and other small grain crops. However, one of the key limitations of N-Sensor is ambient light source. Handheld Green Seeker sensor is the latest addition to the list of crops sensors. The active light source is a major advantage of the Green Seeker sensor. Our preliminary observations on NDVI in relation to canopy development and crop growth in sugarcane are very encouraging and we envisage a potential scope of Green Seeker optical sensor for monitoring crop growth in order to adjust timing and dose of N application for maximizing cane and sugar productivity.

Integrating crop growth simulation and remote sensing to improve resource use efficiency in farming systems

Abstract: Keywords: simulation; model; calibration; remote sensing; radar; optical; satellite; spatial; up-scaling; LAI; nitrogen; chlorophyll; fertilization; precision agriculture; maize; potato; wheat This study investigated the scope and constraints for integrated use of mechanistic crop growth simulation models and earth observation techniques. Integration of high-quality crop growth models and information derived from earth observations can contribute to improved use of resources, reduced crop production risks, reduced environmental degradation, and increased farm income. In the past, both, simulation modelling and remote sensing have been shown to be valuable tools in separate applications in agriculture. Crop growth simulation has made valuable contributions to yield forecasting, proto-typing crop varieties, generation of input-output coefficients for improved agricultural production technologies and to management decision support systems at field level. Likewise, remote sensing techniques have been successfully applied in classification of arable crops and in quantification of vegetation characteristics at different spatial and temporal scales. The starting point of this study was the hypothesis that integration of both techniques would lead to improvements in the dynamic simulation of the crop-soil system and thus contribute to improvements in management decision support systems for environmentally sound agricultural production. Thus far, mutually beneficial linkages have been limited to land use classification via remote sensing (choice of adequate model) and quantification of crop growth and development curves using e.g. estimates of leaf area indices derived from remote sensing images for model calibration under (usually) favourable growth conditions. Only a few studies have considered the potentials of remote sensing for model initialization of growth and development characteristics of a specific crop. In this thesis these potentials have been extended to a more continuous approach, in which remote sensing information is not only used in model initialization, but also in model calibration in the course of the simulation run, so-called run-time calibration. During such a run-time calibration procedure, simulated values of e.g. leaf area index (LAI) and canopy nitrogen status (CNS) are replaced by values estimated from remote sensing images acquired at different stages in the course of the growing period. LAI and CNS are important controlling variables in models for arable crops such as wheat, potato and maize. This run-time calibration procedure has been performed for a full crop growth cycle, for optimal as well as sub-optimal growth conditions. This approach enables spatial differentiation in crop growth simulation, as variations in crop status, resulting from differences in growth conditions, lead to differences in remote sensing signals. The relationships between near and remote sensing observations at leaf, plant and canopy level have been investigated and the effects of variations in estimated values of LAI and CNS used in run-time calibration of dynamic crop growth simulation models on final model results (e.g. crop yield) have been analyzed. Results from potato trials in the Netherlands show that leaf nitrogen contents derived from near sensing observations can be up-scaled to plant and canopy nitrogen status by taking into account the vertical nitrogen distribution in the crop. A vertical nitrogen extinction coefficient ( k n ) of 0.41 resulted in an accuracy increase of the relation between leaf nitrogen (g N m -2 leaf) and SPAD readings (a near sensing technique at leaf level), with a correlation coefficient (r 2 ) of 0.91. Remote sensing observations integrate nitrogen contents over canopy depth and do not require adjustment for vertical nitrogen gradients, if canopy nitrogen status is expressed in total nitrogen content per unit of soil surface. The red edge position (an index derived from remote sensing observations) could be related to canopy nitrogen content (g N m -2 soil) with a correlation coefficient (r 2 ) of 0.82. Leaf area indices of potato (Netherlands) and maize (Argentina, France, USA) crops, for use in run-time calibration, were also accurately derived from field, airborne and spaceborne remote sensing platforms. Introducing LAI values derived from RS in the simulation model and concurrently adjusting CNS by retaining leaf N-concentrations, led to more accurate simulation results for CNS than without such adjustment. The different crops, and the range in environmental conditions, soil fertility status and management practices that were examined in the different case-studies in this thesis, have demonstrated the broad applicability of mechanistic simulation models integrated with remote sensing information Winter wheat fields, wheat phenological stages (emergence, flowering) and management operations (harvest) were successfully identified on the basis of information from optical and radar remote sensing data in a case-study in South-eastern France. Timing of these phenological stages and management operations is important in model calibration as they mark the length of the crop growth period and of the grain-filling period, which are co-determinants of grain yield. At flowering, C-band radar backscatter from the soil is maximally reduced by canopy moisture content. This characteristic was successfully used to estimate regional wheat flowering dates. Integration of RS data in the (point-based) crop growth simulation model allowed its spatial application for prediction of wheat production at regional scale. The estimated value was in agreement with regional yield statistics. This integration thus allows expansion of the application area of valuable research tools, as up-scaling has become feasible. Introduction of remote sensing-based estimates of LAI and CNS in the course of the growing seasons into dynamic simulation of the growth of potato and maize resulted in improved simulation accuracy for aerial crop characteristics, as well as for variables that could not be directly observed by remote sensing, such as soil inorganic nitrogen contents. The degree of success and robustness of the integrated approach depends on the timing, accuracy and number of remote sensing observations available for re-setting the relevant state variables in the course of the simulation period. Simulation accuracy was positively correlated with the number of observation dates from remote sensing. Remote sensing observations around flowering had more impact on calculated final grain yield (FGY) for maize than earlier or later observations. The investigations reported in this thesis have shown that the accuracy of predictions of dynamic and mechanistic crop growth simulation models significantly improves through integrating earth observation-derived information as input for the models and for their run-time calibration. Such integration not only yields more accurate estimates of crop bio-physical variables, such as leaf area index and canopy nitrogen status, but also contributes to improved prediction at regional scales. Such models, producing reliable, site-specific predictions of crop performance and crop requirements are thus effective tools in the development of environmentally-friendly production methods and in optimizing the use of our natural resources. Further research should focus on the scope for estimating additional crop variables of interest for integration in simulation modelling through remote sensing. Management interventions may be triggered by various crop characteristics, such as: 1) canopy temperatures derived from thermal remote sensing systems as an indicator for water stress, 2) canopy discolouring derived from optical remote sensing systems as an indicator for nutrient shortages and 3) canopy architecture derived from radar remote sensing images as an indicator for water and nutrient supply. Remote sensing is also a valuable technique to identify spatial patterns of crop performance and crop status within arable fields. Moreover, remote sensing allows identification of patterns that may be related to specific diseases or special events, such as outbreaks of phytophtera in potato, or lodging in grain crops. This study has demonstrated that a decision support system for crop and soil management based on the integration of crop growth simulation modelling and remotely sensed data is within reach. In addition, nitrogen uptake, its vertical distribution within the crop, and the inorganic nitrogen content of the soil can be simulated more accurately with such an integrated system. Such a decision support system can be used for fine-tuning of fertilizer regimes thus contributing to more environmentally sound and sustained agricultural production.

A special vegetation index for the weed detection in sensor based precision agriculture.

Abstract: Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF).

The Variable-Rate Input Application Decision for Multiple Inputs with Interactions

Abstract: Research has evaluated the relative profitability of variable-rate (VRT) versus uniform-rate (URT) application of a single input in fields with multiple management zones. This study addresses map-based VRT decisions for multiple inputs in fields with multiple management zones. The decision-making framework is illustrated for nitrogen and water applied to irrigated cotton in fields with three management zones. Results suggest traditional methods of determining VRT application of a single input may by suboptimal if interactions exist among VRT inputs and URT inputs. Implications are that a systems approach to multiple-input VRT decisions can produce increased net returns to VRT.

Precision Farming with Geoinformatics: A New Paradigm for Agricultural Production in a Developing Country

Abstract: This paper critically evaluates conventional agricultural land suitability and appraisal methods in a developing country and points out their shortcomings. It also evaluates the new paradigm of precision farming with geoinformatics techniques and highlights the beneficial aspects to land use and agricultural production. In this context the paper shows that traditional physiographic, taxonomic, and land use systems prove inadequate while geoinformatics (using remote sensing data and GPS controlled point observations and soil sample data) work best in modern precision farming. An application of the new paradigm in the Orle River basin in Edo State, Nigeria is used to illustrate how precision farming strategies incorporating geoinformatics might be implemented in Africa.

Precision Agriculture: An Overview

Abstract: Agriculture dominates the world’s land use decisions. The urgent need for doubling farm production over the next 25 years on less land with less water through further intensification would inevitably involve substantial social, economic, and environmental costs. Identification of tools to minimize such costs through enhanced productivity and economic profits while simultaneously conserving the environment is, therefore, crucial. Precision agriculture (PA) is one of such tools catching worldwide attention since the early 1990s. Research interest in PA grew so rapidly that by July 2005 as many as seven international conferences in the United States and five European conferences in the United Kingdom, Denmark, France, Germany, and Sweden respectively, and one Asian conference in Malaysia were held. Further, an international journal Precision Agriculture is published by Springer Netherlands (formerly by Kluwer Academic Publishers).

2006 Precision Agricultural Services Dealership Survey Results

Abstract: Precision technologies are now well-integrated into the agricultural industry - both at the farm level and at the crop input dealer level. No longer are crop input dealers only using the technologies to bring new services to their customers, they are also utilizing the technology in their own businesses to improve the efficiency and effectiveness of their business operations. In early 2006, Crop Life magazine and Purdue University's Center for Food and Agricultural Business conducted a survey for the 11th consecutive year to assess the adoption of precision agriculture practices in the U.S. from the perspective of the retail crop input dealer. The questionnaire was sent to 2500 retail crop input dealerships across the U.S. A total of 368 questionnaires were returned, with 343 being usable providing an effective response rate of 14 percent. Consistent with previous surveys, dealers were asked questions about the types of precision services they offer and/or use in their businesses, the fees they are charging for precision services, how fast their customers are adopting precision agriculture practices, how profitable they are finding precision services to be in their businesses and how their precision customers compare with their 'traditional' customers.

Spatial Data Mining of Colocation Patterns for Decision Support in Agriculture

Abstract: Computer technologies have been introduced into the area of agriculture recently. Precision agriculture, as an example, is a popular concept of using GIS, GPS and other new technologies in helping farmers optimize agricultural production. Colocation pattern mining is a technique for discovering relationships between different thematic features in a spatial domain. For example, an observation that large cities are often close to riversides is obtained with a reliable statistic. Such desired capability is of importance in agricultural applications, like insect pest management. In this paper, a two-phase hierarchical clustering method is proposed to assist people in making decisions based on spatial colocation patterns implicitly existing inside the geographical data sets. It is designed to be a generic system for any data sets in point format. In the first phase, the point features being close together are grouped into a number of clusters. An LC matrix is generated to describe the relationship between the clusters and the layers of feature points. The LC matrix is then analyzed by the second hierarchical clustering to generate a dendrogram. The support and confidence of each single cluster in the dendrogram are calculated to show the concurrent occurrence of features, regardless of their geographical locations.