The identification of homogeneous management zones (MZs) within a field is a basis for site-specific management (SSM). We
assessed the method of defining MZs based on the spatio-temporal homogeneity of six soil properties and above-ground biomass
data from paddy rice, winter wheat and soybean over 3 years on a farm with 124 contiguous small paddy fields. The soil data
were recorded at 372 soil sampling sites on a rectangular grid over the farm. A non-hierarchical cluster analysis was applied
to the soil data and the algorithm grouped the sites into three clusters with similar soil properties. These clusters represent
soil fertility and soil drainage. The three clusters were not randomly distributed across the fields, but formed contiguous
areas associated with landscape position. This was due to the spatial variation of the soil in the study area. We delineated
five MZs based on the spatial structure of the soil heterogeneity of the study area. The validity of the MZs was evaluated
using the biomass data from paddy rice, winter wheat and soybean in each MZ; this depended mainly on soil fertility when conditions
were dry. When the growing season precipitation was greater than the 10-year average, the biomass of winter wheat and soybean
depended on soil drainage. This suggested that the delineation of MZs for site-specific management in fields under a paddy-upland
crop rotation system should be based on several soil properties. The biomass data from the three crops over 3 years was not
effective for delimiting MZs.
Machine vision has been successfully used for mechanical destruction of weeds between rows of crops. Knowledge of the position
of the rows where crops should be growing and the assumption that plants growing outside such positions are weeds may be used
in such systems. However for many horticultural crops, the automatic removal of weeds from inside a row or bands of crops
in which the weeds are mixed with plants in a random manner is not solved. The aim of this study was to verify that plant
height is a discriminating parameter between crop and weed at early growth stages, as weeds and crops grow at different speeds.
Plant height was determined by using an active stereoscopy technique, based on a time multiplexing coded structured light
developed to take into account the specificities of the small scale scene, namely occlusion and thin objects, internal reflections
and high dynamic range. The study was conducted on two carrot varieties sown at commercial density. Different weed species
were present at the time of data acquisition. To accurately represent plant height taking into account the ground irregularities,
a new parameter called ‘corrected plant height’ was computed. This parameter was the distance between plant pixels and the
actual ground level under them obtained by fitting a surface and seen from a reconstructed point of view corresponding to
a camera’s optical axis perpendicular to the ridge plane. The overall classification accuracy without correction was 66% whereas
it reached 83% by using the corrected plant height.
KeywordsWeed detection–Plant height–Stereoscopy–Coded structured light
There is an increasing interest in using 3D computer vision in precision agriculture. This calls for better quantitative evaluation
and understanding of computer vision methods. This paper proposes a test framework using ray traced crop scenes that allows
in-depth analysis of algorithm performance and finds the optimal hardware and light source setup before investing in expensive
equipment and field experiments. It was expected to be a valuable tool to structure the otherwise incomprehensibly large information
space and to see relationships between parameter configurations and crop features. Images of real plants with similar structural
categories were annotated manually for comparison in order to validate the performance results on the synthesised images.
The results showed substantial correlation between synthesized and real plants, but only when all error sources were accounted
for in the simulation. However, there were exceptions where there were structural differences between the virtual plant and
the real plant that were unaccounted for by its category. The test framework was evaluated to be a valuable tool to uncover
information from complex data structures.
Existing experimental methods based on the measurement of crop temperature to estimate water stress have been applied for 20 years. However, the application of such techniques is limited because they are not able to totally overcome either soil interference on the measured signal or directional effects involved in temperature measurements according to sun/sensor angles configuration and crop structure. An energy balance model, based on the 3D description of plants at leaf level, is used to simulate directional cotton crop temperature variability according to crop structure and water status. The model is implemented with a bare soil compartment so that soil temperature, water balance as well heat exchanges with the crop can be computed. Once validated, this approach provides an accurate interpretation of thermal infrared information considering the directional effects involved in surface temperature measurements. This offers the opportunity of analyzing the limits of using temperature-based crop water status indices when dealing with partially covering crops. This study underlines the knowledge and tools to be further investigated in order to improve or perform such experimental techniques.
A world-wide need to use water resources efficiently necessitates more effective approaches to study water and contaminant
transport in soil. This study examined the effectiveness of a multi-receiver electromagnetic induction probe (Geonics EM31-3RT)
and modeling software (EMIGMA) to delineate hydrological regimes at field scale. The site consisted of 20 (15m×15m) tile-drained
plots in Southern Ontario, Canada. Measurements of apparent soil electrical conductivity (ECa) and magnetic susceptibility were obtained using the EM31-3RT in each plot at four distances (0, 2.25, 4.5 and 7.5m) from
the tile drain, and on three occasions (August 22, 26 and 29) in 2003. The EMIGMA was used to simulate a depth profile of
electrical conductivity (ECs) from EM31-3RT readings. The near-surface soil showed significantly (p<0.01) smaller ECa values than at greater depth. The ECa measurements made directly over the tile drains were smaller than those observed further away due to the presence of the
drains. Cluster analysis indicated that the largest ECa values were at the lower elevations of the site related to the redistribution of moisture from higher elevations. The effect
of tile drains and rainfall events on ECa was simulated well by EMIGMA, with smaller ECs values above the drains compared to further away, and showing an increase in ECs in the near-surface soil after rain. This study suggests that EM31-3RT measurements combined with EMIGMA simulation of electrical
conductivity can provide valuable information on depth profiles of ECa and water dynamics in soil.
KeywordsApparent soil electrical conductivity (ECa)–Geonics EM31-3RT electromagnetic induction probe–EMIGMA model–Simulated electrical conductivity (ECs)
Technology adoption in precision agriculture has received considerable attention, while abandonment has received little. Survey
data are now available to evaluate adoption and abandonment decisions. Understanding the factors motivating technology adoption
and abandonment has implications for educational efforts directed toward improving the efficiency of production inputs and
for research and development to improve the value of precision agriculture technologies. The objective of this research was
to identify factors motivating the adoption and abandonment of grid soil sampling in precision cotton production. These decisions
were evaluated assuming a random utility model. Data were obtained from a 2005 survey of cotton producers in 11 Southeastern
states in the USA. Results from limited dependent variable regressions indicate that younger producers who farmed more cotton
area, owned more of their cropland, planted larger amounts of non-cotton area, used a computer for farm management and used
a Personal Digital Assistant (PDA) in the field were more likely to adopt grid soil sampling for cotton precision farming.
Results also suggest that producers with more cotton area who owned livestock and adopted management zone soil sampling were
more likely to abandon grid soil sampling, while those who used a PDA in the field, used grid soil sampling for more years
and followed up grid soil sampling with variable-rate fertilizer application were less likely to abandon grid soil sampling
for cotton production.
KeywordsAdoption-Abandonment-Cotton-Fertilizer-Grid soil sampling-Probit
Inadequate information on factors affecting crop yield variability has contributed to the slow adoption of site-specific farming (SSF). This study was conducted to determine the effects of biotic and abiotic factors on the spatial and temporal variability of irrigated corn grain yields and to derive information useful for SSF. The effects of water (80% evapotranspiration (ET) and 50% ET), hybrid (drought-tolerant and -susceptible), elevation, soil index (SI)(texture), soil NO3–N, arthropods, and diseases on corn grain yield were investigated at Halfway, TX on geo-referenced locations. Grain yields were influenced by interrelationships among biotic and abiotic factors. Grain yields were consistently high under high water treatment, at higher elevations, and on soils with high SI (high clay and silt). Soil NO3–N increased grain yields when water was adequate. Management zones for variable rate fertilizer and water application should, therefore, be based on information on elevation, SI, and soil NO3–N. The effects of arthropods, diseases, and crop stress (due to drought and N) on corn grain yield were unpredictable. Spider mite (Oligonychus pratensis) and common smut (Ustilago zeae) damage occurred under hot and dry conditions in 1998. Spider mite infestations were high in areas with high soil NO3–N. Moderate air temperatures and high relative humidity in 1999 favored southwestern corn borer (Diatraea grandiosella) and common rust (Puccinia maydis) incidences. Knowledge of conditions that favor arthropods and diseases outbreak and crop stress can improve the efficiency of scouting and in-season management of SSF. Management of SSF can be improved when effects of biotic and abiotic factors on grain yield are integrated and evaluated as a system.
Software was developed to spatially assess key crop characteristics from remotely sensed imagery. Sectioning and Assessment
of Remote Images (SARI®), written in IDL® works as an add-on to ENVI®, has been developed to implement precision agriculture strategies. SARI® splits field plot images into grids of rectangular “micro-images” or “micro-plots”. The micro-plot length and width were
defined as multiples of the image spatial resolution. SARI® calculates different indicators for each micro-plot, including the integrated pixel digital values. Studies on weed patches
were done with SARI® using ground-truth data and remote images of two wheat plots infested with Avena sterilis at LaFloridaII and Navajas (Southern Spain). Patches of A. sterilis represented 47.5 and 19.2% of the field areas at the two locations, respectively; the infested areas were a combination of
a few large and several small patches. At LaFloridaII, 2.1% of all patches were >500m2 and 55.0% of all patches were smaller than 10m2. Based on ground-truth weed abundance data, SARI® output includes geo-referenced and visual herbicide prescription maps, which could be used with variable-rate application
KeywordsSARI® add-on software–Remote sensing–Weed spatial distribution–Herbicide prescription maps
The adoption of precision farming (PF) was studied on the basis of personal interviews conducted at several agricultural exhibitions
in Germany. Between 6.65% and 11% of the interviewed farmers used PF. The majority used data collection techniques such as
GPS-based area measurement and soil sampling rather than variable rate application techniques such as site-specific sowing
and fertilising. Roughly half the farmers interviewed knew about PF. About 7–10% of them intended to start using PF in the
future. The results indicated that a large number of farmers did not even know what PF meant. In order to get more insight
into this situation, several interviews were conducted with farmers already using PF techniques. A further study in 2005 of
PF education in Germany indicated that, especially at vocational and technical schools, the subject was only covered to a
small extent although the aim was to give a better coverage in future. At higher education levels, such as at universities
and technical colleges, the teaching of PF was clearly better established. In order to promote awareness of PF among farmers,
information and teaching materials adapted to the relevant educational levels were developed and tried out at training events.
The main topics addressed were parallel tracking systems, site-specific nitrogen fertilising, yield mapping in grain production
and the use of PDAs in crop farming. Finally, preliminary survey results are presented showing how knowledge about PF can
best lead to its adoption and transfer into daily practice.
Tools to quantify the nitrogen (N) status of a rice canopy during inter-nodal elongation (IE) would be valuable for mid-season
N management because N accounts for the largest input cost. The objective of this paper was to study canopy reflectance as
a potential tool for assessing the mid-season status of N in a rice crop. Three field plot experiments were conducted in 2002
and 2003 on cultivars Wells and Cocodrie to study the canopy reflectance response of rice to plant N accumulation (PNA) during
IE and to identify the wavelengths and vegetation indices that are good indicators of PNA. Each experiment included six pre-flood
N treatments of 0, 33.6, 67.2, 100.8, 133.4 and 168kgNha−1. Rice canopy reflectance, biomass, tissue N concentration and PNA were measured weekly during IE. The wavelengths most strongly
correlated to PNA at the beginning of IE were 937 and 718nm. Several vegetation indices were examined to determine which
were strongly correlated (>0.7) with PNA at the beginning of IE. Multiple linear regression models of PNA on selected vegetation
indices explained 53–85% of the variation in PNA during the first week of IE. This study identifies the best combinations
of vegetation indices for estimating PNA in rice.
KeywordsRice-Nitrogen-Canopy reflectance-Vegetation index (VI)-Inter-nodal elongation
Accurate characterization of soil properties across a field can be difficult, especially when compounded with the diverse landscapes used for pastureland. Indirect methods of data collection have the advantage of being rapid, noninvasive, and dense; they may improve mapping accuracy of selected soil parameters. The objective of this study was to determine if the use of soil electrical conductivity (EC) as a covariate improved mapping accuracy of five soil variables across four sampling schemes and two sampling densities in a central Iowa, USA pasture. In this study, cokriging methods were compared to kriging methods for the measured soil properties of soil pH, available P and K, organic matter and moisture. Maps resulting from cokriging each of the soil variables with soil EC exhibited more local detail than the kriged maps of each soil variable. A small, but inconsistent, improvement occurred in kriging variance and prediction accuracy of non-sampled sites when cokriging was implemented. The improvement was generally greater for soil variables more highly correlated with soil EC. This work indicates that cokriging of EC with less densely and invasively collected soil parameters of P, K, pH, organic matter (OM) and moisture does not consistently and substantially improve the characterization accuracy of pasture soil variability.
The digital elevation model (DEM) is considered by many to be an important base map for a precision farming GIS (Geographic Information System). Previous work has shown that dual-frequency survey grade GPS (Global Positioning System) receivers are capable of rapidly producing accurate positional data from a moving vehicle from which DEM's can be developed. However, this type of GPS receiver is expensive and somewhat difficult to use properly. This paper presents the results of a study to evaluate the potential of using single frequency sub-meter and 2–5 meter horizontal accuracy GPS receivers to enable the farmer to collect multiple passes of GPS data during normal field operations from which DEM's can be developed. The results show that when using a single frequency sub-meter GPS receiver: (1) it is possible to develop a DEM with standard deviation of the elevation accuracy on the order of 0.12–0.14 m, (2) in order to collect data with this level of elevation accuracy, data should only be collected when the GDOP (Geometric Dilution Of Precision) is less than 5.0, (3) at least 10 passes of data with appropriate data averaging is required to produce this level of elevation accuracy. The results also indicate that the vertical error associated with a 2–5 meter horizontal accuracy GPS receiver is such that it is not recommended for use in collecting data to develop DEM's in this application.
Management decisions, such as subsoil liming or varying fertilizer inputs to take account of soil depth and anticipated yields
require knowledge of where subsoil constraints to root growth occur across the field. We used selected yield maps based on
criteria derived from crop simulation, apparent soil electrical conductivity (ECa), gamma-ray emission maps and a soil type map drawn by the grower to predict the spatial distribution of subsoil acidity
and shallow soil across a field. Yield maps integrate the effects of variation in soil and climate, and it was only under
specific seasonal conditions that subsoil constraints depressed yields. We used crop simulation modelling to select yield
maps with a large information content on the spatial distribution of these constraints and to omit those with potentially
misleading information. Yield and other spatial data layers were used alone or in combination to develop subsoil mapping options
to accommodate differences in data availability, access to precision agriculture techniques and the grower’s aptitude and
preference. One option used gamma-ray spectrometry and EM38 survey as a dual-sensing system to improve data interpretation.
Gamma-ray spectrometry helped to overcome the inability of current ECa-based methods to sense soil depth in highly weathered sandy soil over cemented gravel. A feature of the approaches presented
here is the use of grower and agronomist knowledge, and experience to help interpret the spatial data layers and to evaluate
which approach is most suitable and likely to be adopted to suit an individual.
Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management
in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation
modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated
by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop
modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties.
The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from
the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available
yield data. Maps of AWC with a resolution of 10m were produced across a dryland grain farm in Australia. For certain years
and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results
were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape
that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of
additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach
as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense
KeywordsInverse modelling–Soil available water capacity–Meta-modelling–Precision agriculture–Crop growth simulation modelling
A four-year study was conducted from 2000 to 2004 at eight field sites in Montana, North Dakota and western Minnesota. Five
of these sites were in North Dakota, two were in Montana and one was in Minnesota. The sites were diverse in their cropping
systems. The objectives of the study were to (1) evaluate data from aerial photographs, satellite images, topographic maps,
soil electrical conductivity (ECa) sensors and several years of yield to delineate field zones to represent residual soil nitrate and (2) determine whether
the use of data from several such sources or from a single source is better to delineate nitrogen management zones by a weighted
method of classification. Despite differences in climate and cropping, there were similarities in the effectiveness of delineation
tools for developing meaningful residual soil nitrate zones. Topographic information was usually weighted the most because
it produced zones that were more correlated to actual soil residual nitrate than any other source of data at all locations.
The soil ECa sensor created better correlated zones at Minot, Williston and Oakes than at most eastern sites. Yield data for an individual
year were sometimes useful, but a yield frequency map that combined several years of standardized yield data was more useful.
Satellite imagery was better than aerial photographs at most locations. Topography, satellite imagery, yield frequency maps
and soil ECa are useful data for delineating nutrient management zones across the region. Use of two or more sources of data resulted
in zones with a stronger correlation with soil nitrate.
KeywordsNitrogen–Management zone–Imagery–Topography–Electrical conductivity (ECa)–Yield mapping
Ten years after the introduction of zone-based management to take into account within-field phenomena in agronomic practices,
several methodological developments have progressed to the operational level. However, this raises a new scientific question:
how can the relevance of this type of management be evaluated? This paper adapts the concept of a technical opportunity index
to zone-specific management. Based on the characteristics of machinery, zoning opportunity is introduced through a new index
(ZOI) adapted specifically to zone-based management. This index takes into account the operational conditions in which zoning
is applied, together with its associated risks. The results obtained on simulated and real field data highlight the relevance
of this index.
KeywordsOpportunity index–Management zones–Precision agriculture–Decision support tools
Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers
leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthorainfestans to spread to neighboring fields. Therefore, automatic detection and removal of volunteer plants is required. In this research,
an adaptive Bayesian classification method has been developed for classification of volunteer potato plants within a sugar
beet crop. With use of ground truth images, the classification accuracy of the plants was determined. In the non-adaptive
scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively.
In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light
conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose
training data in advance.
KeywordsMachine vision-Adaptive Bayesian classification-Weed detection
The yield map is generated by fitting the yield surface shape of yield monitor data mainly using paraboloid cones on floating neighborhoods. Each yield map value is determined by the fit of such a cone on a neighborhood that looks like a huge butterfly flying along the harvest track. Wide wings of the butterfly guarantee that the map is sufficiently smoothed out across the tracks. The coefficients of regression for modeling the paraboloid cones and the scale parameter are estimated using robust weighted M-estimators where the weights decrease with the distance from one to zero; the latter is at the border of the selected neighborhood. The robust way of estimating the model parameters supersedes a procedure for detecting outliers. For a given neighborhood size, this yield mapping method is implemented by the Fortran program
, which can be downloaded from the web. To obtain the appropriate size of the selected neighborhood, the variance of the yield map values should equal the variance of the true yields, which is the difference between the variance of the raw yield data and the error variance of the yield monitor. It is estimated using a robust variogram on data that have not had the trend removed. Based on investigating butterfly neighborhoods the yield map was optimized if the search radius across the harvest tracks was eight times the swath width. One reason for this wide neighborhood is that the regression used for modeling the paraboloid cones is based on weights that decrease linearly from 1 in the middle to zero at the border of the neighborhood, so only data points close to the middle have a large weight.
Precision agriculture (PA) technologies have been commercially available since the early 1990s. However, not only has the pace of adoption in the US been relatively modest but a surprisingly large number of producers are not familiar with these technologies. Using farm level survey data, this study quantifies the role that awareness plays in the decision to adopt PA technology and allows us to explore the potential for public or private information programs to affect the diffusion of PA. PA adoption and awareness are modeled as jointly determined dichotomous variables and their determinants are estimated using a two-stage (i.e. instrumental variable) logistic specification. The first-stage logit model indicated that operator education and computer literacy, full-time farming, and farm size positively affected the probability of PA awareness while the effect of age was negative. Grain and oilseed farms (i.e. corn, soybean, and small grains) and specialty crop farms (i.e. fruits, vegetables, and nuts) as well as farms located in the Heartland and Northern Great Plains regions were most likely to be aware of PA technologies. The second-stage PA adoption logit model, which included an instrumental variable to account for the endogeneity of awareness, revealed that farm size, full-time farming, and computer literacy positively influenced the likelihood of PA adoption. Grain and oilseed farms were the most likely types of farms to adopt PA as were farms in the Heartland region. Awareness, as defined in this study, was not found to be limiting the adoption of PA, suggesting that farmers for whom the technology is profitable are already aware of the technology and that a sector-wide public or private initiative to disseminate PA information would not likely have a major impact on PA diffusion.
Precision agriculture (PA) technologies are being applied to crops in Brazil, which are important to ensure Brazil’s position
in agricultural production. However, there are no studies available at present to indicate the extent to which PA technologies
are being used in the country. Therefore, the main objective of this research was to investigate how the sugar-ethanol industry
in São Paulo state, which produces 60% of the domestic sugarcane, is adopting and using these techniques. For this purpose,
primary data were used, which were obtained from a questionnaire sent to all companies operating in the sugar-ethanol industry
in the region. The aim was to determine to what extent these companies are adopting and using PA technologies, and also to
promote a more in-depth discussion of the topic within the sugar-ethanol industry. Information was obtained on the features
of the companies, on sources of information that they use for adopting these technologies, on their impacts on these companies
and on obstacles hindering their adoption. The main conclusions of this research suggest that companies that adopt and use
PA practices reap benefits, such as managerial improvements, higher yields, lower costs, minimization of environmental impacts
and improvements in sugarcane quality.
Research on Precision Farming (PF) relates the adoption of PF primarily to economic incentives as well as farm attributes,
whereas social factors are commonly ignored. Therefore, the present study analyses the importance of farmers’ communication
and co-operation strategies in the adoption of PF and their relation to farm attributes. Forty-nine qualitative interviews
with stakeholders from the agricultural sector were conducted. The survey was based in Germany where most interviews took
place and reflected with findings from the Czech Republic, Denmark and Greece. It is revealed that farms differ in their communication
strategies depending on farm size. Joint investment in PF was only reported from some regions. It can be assumed that agricultural
contractors will be major driving forces behind the adoption of PF over the next 10years, especially in areas with smaller-sized
farms. Agricultural data processing by service providers is seen as a common issue. Concerns regarding potential data misuse,
over-regulation and software compatibility were raised.
This research evaluated the factors that influenced cotton (Gossypium hirsutum L.) producers to adopt remote sensing for variable-rate application of inputs. A logit model estimated with data from a 2005
mail survey of cotton producers in 11 southern USA states was used to evaluate the adoption of remote sensing. The most frequently
made management decisions using remote sensing were the application of plant growth regulators, the identification of drainage
problems and the management of harvest aids. A producer who was younger, more highly educated and had a larger farm with irrigated
cotton was more likely to adopt remote sensing. In addition, farmers who used portable computers in fields and produced their
own map-based prescriptions had a greater probability of using remote sensing. The results suggest that value-added map-making
services from imagery providers greatly increased the likelihood of a farmer being a user of remote sensing.
Variable rate application of fertiliser (VR) is a practice underpinning a profitable grains industry in Australia. We updated
the extent of VR adoption through a national survey (n=1130) covering all grain growing regions. Three smaller regional-based surveys (n=39–102) collected detailed information on the nature and reasoning behind the use of various forms of the technology. We
analysed the constraints to the adoption of each step using adoption theory. Surveys showed that 20% of grain growers have
adopted some form of VR (varied from 11–35%), up significantly from <5% found 6years earlier. Adopters are more than likely
to have larger farms with a higher area in cropping. Many non-adopters were convinced of the agronomic and economic benefits
of VR. A significant proportion of growers were managing within-field variability with manually-operated systems rather than
more sophisticated VR technology, and have adopted some form of VR without yield maps, preferring to use soil tests, electro-magnetic
induction or their own knowledge of soil and yield variation to define management. The rate of adoption is expected to continue
to rise based on greater awareness of the benefits of the technology. The constraints to adoption were technical issues with
equipment and software access to service provision and the incompatibility of equipment with existing farm operations.
KeywordsVariable rate technology–Precision agriculture–Australia–Economics–Adoptions–Survey
The adoption of Precision Farming (PF) in Germany has been studied by several mail surveys, telephone interviews and personal
interviews with farmers, advisors, teachers and representatives of the PF industry. The intention was to monitor how PF techniques
have entered the German market over the years and geographic location. The farmers were interviewed about their experiences
with PF technology and their attitudes and barriers towards it. Those farmers who had not yet used PF technologies were asked
for the reason and on which condition they would probably start with PF. Although the number of PF users slightly increased
between 2001 and 2006, the results of all the surveys indicate that there are still various barriers regarding PF. Those farmers
who had already used some PF technologies struggled with many problems at the beginning, but after overcoming these problems,
they were generally content with the introduction of PF technologies. However, the majority of the interviewed farmers hesitated
to introduce PF techniques mainly because of the high costs of the technology. Most of the interviewed teachers at vocational
and technical schools stated that PF was not yet a subject in courses. The interviews with the advisors show that most of
them do not offer any advisory service in the field of PF. Finally, the results of the interviews with representatives of
the agricultural engineering industry confirm the statements of the other surveys.