Precision Agriculture (Journal)

Abstract  A fuzzy inference system (FIS) was developed to generate recommendations for spatially variable applications of N fertilizer. Key soil and plant properties were identified based on experiments with rates ranging from 0 to 250 kg N ha−1 conducted over three seasons (2005, 2006 and 2007) on fields with contrasting apparent soil electrical conductivity (ECa), elevation (ELE) and slope (SLP) features. Mid-season growth was assessed from remotely sensed imagery at 1-m2 resolution. Optimization of N rate by the FIS was defined against maximum corn growth in the weeks following in-season N application. The best mid-season growth was in areas of low ECa, high ELE and low SLP. Under favourable soil conditions, maximum mid-season growth was obtained with low in-season N. Responses to N fertilizer application were better where soil conditions were naturally unfavourable to growth. The N sufficiency index (NSI) was used to judge plant N status just prior to in-season N application. Expert knowledge was formalized as a set of rules involving ECa, ELE, SLP and NSI levels to deliver economically optimal N rates (EONRs). The resulting FIS was tested on an independent set of data (2008). A simulation revealed that using the FIS would have led to an average N saving of 41 kg N ha−1 compared to the recommended uniform rate of 170 kg N ha−1, without a loss of yield. The FIS therefore appears to be useful for incorporating expert knowledge into spatially variable N recommendations.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9188-z
• Authors
  • N. Tremblay, Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, 430 Gouin Blvd, St-Jean-sur-Richelieu, Montreal, QC J3B 3E6, Canada
  • M. Y. Bouroubi, Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, 430 Gouin Blvd, St-Jean-sur-Richelieu, Montreal, QC J3B 3E6, Canada
  • B. Panneton, Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, 430 Gouin Blvd, St-Jean-sur-Richelieu, Montreal, QC J3B 3E6, Canada
  • S. Guillaume, Cemagref, UMR ITAP, 34196 Montpellier, France
  • P. Vigneault, Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, 430 Gouin Blvd, St-Jean-sur-Richelieu, Montreal, QC J3B 3E6, Canada
  • C. Bélec, Horticulture Research and Development Centre, Agriculture and Agri-Food Canada, 430 Gouin Blvd, St-Jean-sur-Richelieu, Montreal, QC J3B 3E6, Canada

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  It has been suggested that apple ( Malus * domestica Borkh) flowering distribution maps can be used for site-specific management decisions. The objectives of this study were (i) to study the flower density variability in an apple orchard using image analysis and (ii) to model the correlation between flower density as determined from image analysis and fruit yield. The research was carried out in a commercial apple orchard in Central Greece. In April 2007, when the trees were at full bloom, photos of the trees were taken following a systematic uniform random sampling procedure. In September 2007, yield mapping was carried out measuring yield per ten trees and recording the position of the centre of the ten trees. Using this data (the measured yield of the trees and the pictures samples, representing the flower distribution), an image processing-based algorithm was developed that predicts tree yield by analyzing the picture of the tree at full bloom. For the evaluation of the algorithm, a case study scenario is presented where the error of the predicted yield was set at 18%. These results indicated that potential yield could be predicted early in the season from flowering distribution maps and could be used for orchard management during the growing season.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9187-0
• Authors
  • A. D. Aggelopoulou, Faculty of Crop Production and Rural Environment, School of Agricultural Sciences, University of Thessaly, Fytoko Str., N. Ionia, 38446 Magnesia, Greece
  • D. Bochtis, Faculty of Agricultural Sciences, Department of Agricultural Engineering, University of Aarhus, Blichers Allé 20, 8830 Tjele, Denmark
  • S. Fountas, Faculty of Crop Production and Rural Environment, School of Agricultural Sciences, University of Thessaly, Fytoko Str., N. Ionia, 38446 Magnesia, Greece
  • K. C. Swain, Faculty of Agricultural Sciences, Department of Agricultural Engineering, University of Aarhus, Blichers Allé 20, 8830 Tjele, Denmark
  • T. A. Gemtos, Faculty of Crop Production and Rural Environment, School of Agricultural Sciences, University of Thessaly, Fytoko Str., N. Ionia, 38446 Magnesia, Greece
  • G. D. Nanos, Faculty of Crop Production and Rural Environment, School of Agricultural Sciences, University of Thessaly, Fytoko Str., N. Ionia, 38446 Magnesia, Greece

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  One approach to the application of site-specific techniques and technologies in precision agriculture is to subdivide a field into a few contiguous homogenous zones, often referred to as management zones (MZs). Delineating MZs can be based on some sort of clustering, however there is no widely accepted method. The application of fuzzy set theory to clustering has enabled researchers to account better for the continuous variation in natural phenomena. Moreover, the methods based on non-parametric density estimation can detect clusters of unequal size and dispersion. The objectives of this paper were to: (1) compare different procedures for creating management zones and (2) determine the relation of the MZs delineated with potential yield. One hundred georeferenced point measurements of soil and crop properties were obtained from a 12 ha field cropped with durum wheat for two seasons. The trial was carried out at the experimental farm of CRA-CER in Foggia (Italy). All variables were interpolated on a 1 × 1 m grid using the geostatistical techniques of kriging and cokriging. The techniques compared to identify MZs were: (1) the ISODATA method, (2) the fuzzy c-means algorithm and (3) a non-parametric density algorithm. The ISODATA method, which was the simplest, subdivided the field into three distinct classes of suitable size for uniform management, whereas the other two methods created two classes. The non-parametric density algorithm characterized the edge properties between adjacent clusters more efficiently than the fuzzy method. The clusters from the non-parametric density algorithm and yield maps for three seasons (2005–2006, 2006–2007 and 2007–2008) were compared and agreement measures were computed. The kappa coefficients for the three seasons were negative or small positive values which indicate only slight agreement. These results illustrate the importance of temporal variation in spatial variation of yield in rainfed conditions, which limits the use of the MZ approach.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9183-4
• Authors
  • F. Guastaferro, CRA-SCA, Research Unit for Cropping Systems in Dry Environments, via C. Ulpiani 5, 70125 Bari, Italy
  • A. Castrignanò, CRA-SCA, Research Unit for Cropping Systems in Dry Environments, via C. Ulpiani 5, 70125 Bari, Italy
  • D. De Benedetto, CRA-SCA, Research Unit for Cropping Systems in Dry Environments, via C. Ulpiani 5, 70125 Bari, Italy
  • D. Sollitto, CRA-SCA, Research Unit for Cropping Systems in Dry Environments, via C. Ulpiani 5, 70125 Bari, Italy
  • A. Troccoli, CRA-CER, Experimental Center for the Cereals, S.S. 16 km 675, 71100 Foggia, Italy
  • B. Cafarelli, Department of Economical, Mathematical and Statistical Science (DSEMS), University of Foggia, Largo Papa Giovanni Paolo II, 71100 Foggia, Italy

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  This paper follows previous research that identified 15 hyperspectral wavebands that were suitable to estimate paddy rice leaf area index (LAI). The objectives of the study were to: (1) test the efficiency of the wavebands selected in the previous study, (2) to evaluate the potential of least squares support vector machines (LS-SVM) to estimate paddy rice LAI from canopy hyperspectral reflectance and (3) to compare multiple linear regression-MLR, partial least squares-PLS regression and LS-SVM to determine paddy rice LAI using the selected wavebands. In the study, measurements of hyperspectral reflectance (350–2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing seasons. On the basis of the wavebands selected previously, models based on MLR, PLS and LS-SVM to estimate rice LAI were compared using the data from 123 observations, which were split randomly for model calibration (2/3) and validation (1/3). Root mean square errors (RMSEs) and the correlation coefficients (r) between measured and predicted LAI values from model calibration and validation were calculated to evaluate the quality of the models. The results showed that the LS-SVM model using the 15 selected wavebands produced more accurate estimates of paddy rice LAI than the PLS and MLR models. We concluded that the LS-SVM approach may provide a useful exploratory and predictive tool for estimating paddy rice LAI when applied to reflectance data using the 15 selected wavebands.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9185-2
• Authors
  • Fu-min Wang, Institute of Hydrology and Water Resources, Zhejiang University, Zijingang Campus, Hangzhou, 310058 China
  • Jing-feng Huang, Research Center of Agricultural Information Science & Technology, Zhejiang University, Huajiachi Campus, 310029 Hangzhou, China
  • Zhang-hua Lou, Institute of Hydrology and Water Resources, Zhejiang University, Zijingang Campus, Hangzhou, 310058 China

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  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 10 m 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 datasets.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9184-3
• Authors
  • M. J. Florin, University of Sydney Australian Centre for Precision Agriculture Lvl. 2 John Woolley Bldg. A20, Manning Rd. University of Sydney NSW 2006 Australia
  • A. B. McBratney, University of Sydney Australian Centre for Precision Agriculture Lvl. 2 John Woolley Bldg. A20, Manning Rd. University of Sydney NSW 2006 Australia
  • B. M. Whelan, University of Sydney Australian Centre for Precision Agriculture Lvl. 2 John Woolley Bldg. A20, Manning Rd. University of Sydney NSW 2006 Australia
  • B. Minasny, University of Sydney Australian Centre for Precision Agriculture Lvl. 2 John Woolley Bldg. A20, Manning Rd. University of Sydney NSW 2006 Australia

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Special issue of papers from the 7th European Conference on Precision Agriculture (ECPA)

• Content Type Journal Article
• Category Editorial
• DOI 10.1007/s11119-010-9182-5
• Authors
  • Margaret A. Oliver, University of Reading Department of Soil Science Whiteknights P.O. Box 233 Reading RG6 6DW UK
  • John Stafford, Silsoe Solutions Ampthill Bedford UK

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256
• Journal Volume Volume 11
• Journal Issue Volume 11, Number 4 / August, 2010

Abstract  This paper examines the prediction of within-field differences in protein in malting barley at a late growth stage using the Yara N-Sensor and prediction of its regional variation with medium resolution satellite images. Field predictions of protein in the crop at a late growth stage could be useful for harvest planning, whereas regional prediction of barley quality before harvest would be useful for the grain industry. The project was carried out in central Sweden where the variation in protein content of malting barley has been documented both within fields and regionally. Scanning with an N-sensor and crop sampling were carried out in 2007 and 2008 at several fields. The regional data used consisted of weather data, quality analyses of the malting barley delivered to the major farmers’ co-operative, crops grown and field boundaries. Satellite scenes (SPOT 5 and IRS-P6 LISS-III) were acquired from a date as close as possible to the N-sensor scans. Reasonable partial least squares (PLS) models could be constructed based on weather and reflectance data from either the N-sensor or satellite. The models used mainly reflectance data, but the weather data improved them. Better field models could be created with data from the N-sensor than from the satellite image, but a local satellite-based model based on a simple ratio (middle infrared/green) in combination with weather was useful in regional prediction of malting barley protein. A regional prediction model based only on the weather variables explained about half the variation in recorded protein.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9181-6
• Authors
  • Mats Söderström, Swedish University of Agricultural Sciences Department of Soil and Environment P.O. Box 234 532 23 Skara Sweden
  • Thomas Börjesson, Lantmännen Lantbruk P.O. Box 30192 104 92 Stockholm Sweden
  • Carl-Göran Pettersson, Lantmännen Lantbruk P.O. Box 30192 104 92 Stockholm Sweden
  • Knud Nissen, Lantmännen Lantbruk P.O. Box 30192 104 92 Stockholm Sweden
  • Olle Hagner, Swedish University of Agricultural Sciences Department of Forest Resource Management 901 83 Umeå Sweden

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  This study examines the potential of hyperspectral sensor systems for the non-destructive detection and differentiation of plant diseases. In particular, a comparison of three fungal leaf diseases of sugar beet was conducted in order to facilitate a simplified and reproducible data analysis method for hyperspectral vegetation data. Reflectance spectra (400–1050 nm) of leaves infected with the fungal pathogens Cercospora beticola, Erysiphe betae, and Uromyces betae causing Cercospora leaf spot, powdery mildew and rust, respectively, were recorded repeatedly during pathogenesis with a spectro-radiometer and analyzed for disease-specific spectral signatures. Calculating the spectral difference and reflectance sensitivity for each wavelength emphasized regions of high interest in the visible and near infrared region of the spectral signatures. The best correlating spectral bands differed depending on the diseases. Spectral vegetation indices related to physiological parameters were calculated and correlated to the severity of diseases. The spectral vegetation indices Normalised Difference Vegetation Index (NDVI), Anthocyanin Reflectance Index (ARI) and modified Chlorophyll Absorption Integral (mCAI) differed in their ability to assess the different diseases at an early stage of disease development, or even before first symptoms became visible. Results suggested that a distinctive differentiation of the three sugar beet diseases using spectral vegetation indices is possible using two or more indices in combination.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9180-7
• Authors
  • A.-K. Mahlein, University of Bonn Institute of Crop Science and Resource Conservation (INRES)—Phytomedicine Nussallee 9 53115 Bonn Germany
  • U. Steiner, University of Bonn Institute of Crop Science and Resource Conservation (INRES)—Phytomedicine Nussallee 9 53115 Bonn Germany
  • H.-W. Dehne, University of Bonn Institute of Crop Science and Resource Conservation (INRES)—Phytomedicine Nussallee 9 53115 Bonn Germany
  • E.-C. Oerke, University of Bonn Institute of Crop Science and Resource Conservation (INRES)—Phytomedicine Nussallee 9 53115 Bonn Germany

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256
• Journal Volume Volume 11
• Journal Issue Volume 11, Number 4 / August, 2010

Abstract  Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m) in northern Turkey. Soil variables considered were CaCO3, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction. The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents, (R 2 values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1, 37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial prediction of soil properties and enables a reduction in sampling and laboratory analyses.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9173-6
• Authors
  • A. Volkan Bilgili, Harran University Department of Soil Science, Agriculture Faculty Sanliurfa 63300 Turkey
  • Fevzi Akbas, Gaziosmanpasa University Department of Soil Science, Agriculture Faculty Tokat 60100 Turkey
  • Harold M. van Es, Cornell University Department of Crop and Soil Science Ithaca NY 14853-1901 USA

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  In this study, protein content (PC) of brown rice before harvest was established by remote sensing (RS) and analyzed to select the key management factors that cause variation of PC using a GIS database. The possibility of finding out the key management factors using GreenNDVI was tested by combining RS and a GIS database. The study site was located at Yagi basin (Japan) and PC for seven districts (85 fields) in 2006 and nine districts (73 fields) in 2007 was investigated by a rice grain taste analyzer. There was spatial variability between districts and temporal variability within the same fields. PC was predicted by the average of GreenNDVI at sampling points (Point GreenNDVI) and in the field (Field GreenNDVI). The accuracy of the Point GreenNDVI model (r 2 > 0.424, RMSE < 0.256%) was better than for the Field GreenNDVI model (r 2 > 0.250, RMSE < 0.298%). A general-purpose model (r 2 = 0.392, RMSE = 0.255%) was established using 2 years data. In the GIS database, PC was separated into two parts to compare the difference in PC between the upper (mean + 0.5SD) and lower (mean − 0.5SD) parts. Differences in PC were significant depending on the effective cumulative temperature (ECT) from transplanting to harvest (Factor 4) in 2007 but not in 2006. Because of the difference in ECT depending on vegetation term (from transplanting to sampling), PC was separated into two groups based on the mean value of ECT as the upper (UMECT) and lower (LMECT) groups. In 2007, there were significant differences in PC at LMECT group between upper and lower parts depending on the ECT from transplanting to last top-dressing (Factor 2), the amount of nitrogen fertilizer at top-dressing (Factor 3) and Factor 4. When the farmers would have changed their field management, it would have been possible to decrease protein contents. Using the combination of RS and GIS in 2006, it was possible to select the key management factor by the difference in the Field GreenNDVI.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9179-0
• Authors
  • Chanseok Ryu, Graduate School of Agriculture Kyoto University Environmental Science and Technology Kitashirakawa Oiwake-cho Sakyo-ku, Kyoto 606-8502 Japan
  • Masahiko Suguri, Graduate School of Agriculture Kyoto University Environmental Science and Technology Kitashirakawa Oiwake-cho Sakyo-ku, Kyoto 606-8502 Japan
  • Michihisa Iida, Graduate School of Agriculture Kyoto University Environmental Science and Technology Kitashirakawa Oiwake-cho Sakyo-ku, Kyoto 606-8502 Japan
  • Mikio Umeda, Kyoto University Yoshida Hon-machi Sakyo-ku, Kyoto 606-8501 Japan
  • Chungkeun Lee, National Academy of Agricultural Science, RDA Suwon 441-707 Korea

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  Precision irrigation requires the mapping of within-field variations of water requirement. Conventional remote sensing techniques provide estimates of water status at only shallow soil depths. The ability of a water sensitive crop, sugar beet, to act as an intermediate sensor providing an integrated measure of water status throughout its rooting depth is tested here. Archive aerial photographs and satellite imagery of Eastern England show crop patterns resulting from past periglacial processes. The patterns were found to be spatially and temporally consistent. Field sampling of soil cores to 1 m depth established that the within-field wilting zones were significantly associated with coarser or shallow soils. The stress classes, determined by classification of the digitised images, were weakly correlated with total available water (Pearson correlation r = 0.588, P < 0.05). These results suggest that wilting in sugar beet can be used as an intermediate sensor for quantifying potential soil water availability within the root zone. Within-field stress maps generated in 1 year could be applied as a strategic tool allowing precision irrigation to be applied to high-value crops in following years, helping to make more sustainable use of water resources.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9177-2
• Authors
  • L. Zhang, University of Nottingham School of Geography University Park Nottingham NG7 2RD UK
  • M. L. Clarke, University of Nottingham School of Geography University Park Nottingham NG7 2RD UK
  • M. D. Steven, University of Nottingham School of Geography University Park Nottingham NG7 2RD UK
  • K. W. Jaggard, Higham, Bury St. Edmunds Broom’s Barn Research Centre Suffolk IP28 6NP UK

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  This paper proposes a general wireless sensor network architecture for monitoring horticultural crops that are distributed among small plots scattered at distances of up to 10 km from one another. The technology used for the real implementation of the architecture is based on the B-MAC (Berkeley Medium Access Control) medium access protocol to assure a high degree of sensor node power autonomy. To resolve this issue, a series of specialized sensor nodes (Soil-Mote, Environmental-Mote and Water-Mote) have been developed along with a gateway to interconnect them with the farm central offices. Before starting device development, simulations were conducted to ensure that acceptable performance would be achieved with the selected technology in terms of node autonomy, achieved throughput and delays. To that end, it was necessary to implement the selected B-MAC protocol in the ns-2 (Network Simulator-2) simulation framework. The final system was deployed on a real crop to check and validate the simulation results against experimental results.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9178-1
• Authors
  • Juan A. López, Technical University of Cartagena Campus Muralla del Mar s/n 30202 Cartagena Spain
  • Antonio-Javier Garcia-Sanchez, Technical University of Cartagena Campus Muralla del Mar s/n 30202 Cartagena Spain
  • F. Soto, Technical University of Cartagena Campus Muralla del Mar s/n 30202 Cartagena Spain
  • A. Iborra, Technical University of Cartagena Campus Muralla del Mar s/n 30202 Cartagena Spain
  • Felipe Garcia-Sanchez, Technical University of Cartagena Campus Muralla del Mar s/n 30202 Cartagena Spain
  • Joan Garcia-Haro, Technical University of Cartagena Campus Muralla del Mar s/n 30202 Cartagena Spain

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  This paper describes a method that allows farmers to (i) decide whether or not the spatial variation of a field allows a reliable variable-rate application, (ii) discover if a particular threshold (field segmentation) based on within-field data is technically feasible according to the application machinery and (iii) make an appropriate application map. Our method aims to improve on a previous technical opportunity index (Oi) with a fuzzy technical opportunity index (FTOi). The FTOi considers (i) a fuzzy footprint model of a variable-rate application controller (VRAC), which describes the area within which the VRAC can operate reliably, (ii) the location inaccuracy of the data and (iii) the ability (accuracy) of the VRAC to perform distinct levels of treatments. The originality of our approach is based on the use of a fuzzy estimation process to decide if a level of treatment is reliable or not for each area over which the VRAC operates. A unique feature of the approach is that it does not require data on a regular grid. Only characteristics of the machinery and the treatment to be applied are necessary; interpolation of the data and geostatistics are not required by the end user. Tests on theoretical fields, obtained from a simulated annealing procedure, showed that the FTOi was able to assess the technical manageability of variation in the field. Tests also showed that our approach could take into account problems related to low resolution data. Finally, the approach has been applied to a real situation in a vineyard block. This has highlighted the practical implementation and the ability to generate useful information for managing the within-field variation (optimal thresholding, and application and error maps).

• Content Type Journal Article
• DOI 10.1007/s11119-010-9176-3
• Authors
  • J. N. Paoli, AgroSup Dijon UP GAP 26 Bd Dr Petitjean BP 87999 21079 Dijon Cedex France
  • B. Tisseyre, Montpellier SupAgro/Cemagref UMR ITAP 2 Place Viala 34060 Montpellier Cedex France
  • O. Strauss, LIRMM Department of Robotics 161, Rue Ada 34392 Montpellier Cedex 5 France
  • A. B. McBratney, The University of Sydney Australian Centre for Precision Agriculture John Woolley Building A20 Sydney NSW 2006 Australia

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256
• Journal Volume Volume 11
• Journal Issue Volume 11, Number 4 / August, 2010

Abstract  A simple and transparent analytical model for C3-crop biomass accumulation is introduced. The model is aimed to be used as a decision tool in precision farming. It is valid when growth is limited only by radiation or water and gives the optimal (maximum) biomass. It contains 8 fixed parameters, all with a clear basis in physics, chemistry and physiology. As a function of time, the growth is divided into two phases: exponential and linear. At an early stage of growth, the growth is exponential due to the expanding leaf area of the crop. At this stage, the model needs 6 parameters. The growth becomes linear when the leaf area is adequate to use all possible radiation. The model needs 2 parameters at this later stage. Water-limited growth needs an additional set of 4 parameters to describe phenomena of water related processes. When water is a limiting factor, the root-growth model becomes critical because the daily root growth determines the crop’s growth directly. The model was tested first against field data at one point where all relevant inputs and parameters of the model were measured. Despite the simplicity of the model, there was a good agreement between simulated and measured values of biomass and leaf area. A scenario is described to show how the model may be used in practice and what kind of field data is needed. In on-line precision farming the key factor is the amount of radiation used by the crop, which can be measured adequately with two sensors, one above and one below the canopy.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9174-5
• Authors
  • Mikko Hautala, University of Helsinki Department of Agricultural Sciences, Faculty of Agriculture and Forestry P.O. Box 28 00014 Helsinki Finland
  • Mikko Hakojärvi, University of Helsinki Department of Agricultural Sciences, Faculty of Agriculture and Forestry P.O. Box 28 00014 Helsinki Finland

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256

Abstract  Sensor-based methods of analysis to assess dry matter yield and quality constituents of crops are time- and labour-saving, and can facilitate site-specific management. Nevertheless, standard nadir measurements of maize (Zea mays cv. Ambrosius), based on top-of-canopy reflectance, are difficult due to plant heights of more than three metres. This study was conducted to explore the potential of off-nadir field spectral measurements for the non-destructive prediction of dry matter yield (DM), metabolisable energy (ME) and crude protein (CP) in total biomass in a maize canopy. Plants were measured at five different heights (0–50, 50–100, 100–50, 150–200 and 200–250 cm above the soil) at three zenith view angles (60°, 75° and 90°, respectively). Modified partial least squares regression was used for analysis of the hyperspectral data (355–2300 nm and 620–1000 nm). Optimum combinations of angle and height as well as an optimum one-sensor-strategy were determined for DM yield, CP and ME in total biomass. Coefficients of determination for off-nadir measurements were compared to nadir measurements; the results showed improved prediction accuracies for DM yield and ME using off-nadir measurements, but not for CP for which nadir measurements were better.

• Content Type Journal Article
• DOI 10.1007/s11119-010-9175-4
• Authors
  • Daniela Perbandt, University of Kassel Department of Grassland Science and Renewable Plant Resources Steinstr. 19 37213 Witzenhausen Germany
  • Thomas Fricke, University of Kassel Department of Grassland Science and Renewable Plant Resources Steinstr. 19 37213 Witzenhausen Germany
  • Michael Wachendorf, University of Kassel Department of Grassland Science and Renewable Plant Resources Steinstr. 19 37213 Witzenhausen Germany

• Journal Precision Agriculture
• Online ISSN 1573-1618
• Print ISSN 1385-2256