Description: R. Webster and M. Lark, Field sampling for environmental science and management Content Type Journal Article Category Book Review Pages 1-2 DOI 10.1007/s11119-012-9293-2 Authors Marc Van Meirvenne, Department of Soil Management, Ghent University, Coupure 653, 9000 Ghent, Belgium Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256

Description:    The research reported here seeks to determine whether it is necessary to obtain optical reflectance measurements with a GreenSeeker ® handheld sensor from each field to make accurate in-season nitrogen application recommendations for winter wheat, and how much precision—and profit—would be lost by moving from site-specific (or field-specific) optical reflectance sampling to region-level sampling. The approach used was to estimate a separate linear response-plateau regression every year using yield and optical reflectance data from randomized complete block experiments. Profits from region-level sampling and field-level sampling were statistically indistinguishable, but this result was mostly due to both being imprecise. Furthermore, the region- and field-based sampling systems were no better than break-even with the historical extension advice to apply preplant anhydrous ammonia at 90 kg ha −1 . The approach of estimating a new regression every year is too imprecise, whether at the field or region level. This research goes beyond past research by accounting for the uncertainty in the estimated relationships. The poor performance of the systems is directly related to the imprecise relationship between yield and optical reflectance responses to nitrogen. Content Type Journal Article Pages 1-25 DOI 10.1007/s11119-012-9291-4 Authors D. C. Roberts, Department of Agribusiness & Applied Economics, North Dakota State University, NDSU Dept. 7610, P. O. Box 6050, Fargo, ND 58108-6050, USA B. W. Brorsen, Department of Agricultural Economics, Oklahoma State University, 414 Agricultural Hall, Stillwater, OK 74078, USA J. B. Solie, Department of Biosystems and Agricultural Engineering, Oklahoma State University, 111 Agricultural Hall, Stillwater, OK 74078, USA W. R. Raun, Department of Plant and Soil Sciences, Oklahoma State University, 044 N. Agricultural Hall, Stillwater, OK 74078, USA Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256

Description:    Agricultural machines spend a significant part of their time on non-productive operations such as maneuvering near the boundaries of the field and loading or offloading of inputs or outputs (here referred to as servicing). This paper integrates existing methods for route optimization so as to minimize the time spent on turns and machine servicing on fields cultivated in straight rows. The following variables are optimized: (1) the orientation (angle) of the tracks, (2) the order of tracks, and (3) the types of turns between tracks. The angle of the tracks relative to field boundaries influences the number and lengths of the machine tracks, the number of turns and the positions where the machine can be serviced. Track order and the type of turns are selected to achieve overall efficiency. The algorithm was tested by computing routes for a set of fields of different sizes and assuming different operations. On small fields that do not require servicing, optimizing the turns between tracks resulted in a reduction of up to 50 % in turning time compared to the prevailing practice of navigation between adjacent tracks. A comparison of two sprayers in terms of servicing efficiency suggested that the algorithm can help selecting machinery for given field geometries. In some cases requiring machine servicing, the track orientation giving the shortest turning time did not produce the least servicing time. This illustrates that machine servicing should be taken into consideration for global optimization of machine traffic. Content Type Journal Article Pages 1-21 DOI 10.1007/s11119-012-9290-5 Authors Mark Spekken, Biosystems Engineering Department, ESALQ, Universidade de São Paulo, Piracicaba, São Paulo, Brazil Sytze de Bruin, Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Wageningen, The Netherlands Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256

Description:    Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31.7 ha from which samples were collected at depths of 0.00–0.20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30.4l ha −1 ) and best coffee beverage quality (61 sacks ha −1 ). Based on the results, we believe that multivariate analysis, geostatistics and the soil–relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production. Content Type Journal Article Pages 1-14 DOI 10.1007/s11119-012-9288-z Authors Maria Gabriela Baracat Sanchez, Dept. Solos e Adubos, Faculdade de Ciências Agrárias e Veterinárias, Unesp - Univ Estadual Paulista, Campus de Jaboticabal, Jaboticabal, SP 14870-900, Brazil José Marques Jr., Dept. Solos e Adubos, Faculdade de Ciências Agrárias e Veterinárias, Unesp - Univ Estadual Paulista, Campus de Jaboticabal, Jaboticabal, SP 14870-900, Brazil Diego Silva Siqueira, Dept. Solos e Adubos, Faculdade de Ciências Agrárias e Veterinárias, Unesp - Univ Estadual Paulista, Campus de Jaboticabal, Jaboticabal, SP 14870-900, Brazil Livia Arantes Camargo, Dept. Solos e Adubos, Faculdade de Ciências Agrárias e Veterinárias, Unesp - Univ Estadual Paulista, Campus de Jaboticabal, Jaboticabal, SP 14870-900, Brazil Gener Tadeu Pereira, Dept. Ciências Exatas, Faculdade de Ciências Agrárias e Veterinárias, Unesp - Univ Estadual Paulista, Campus de Jaboticabal, Jaboticabal, SP 14870-900, Brazil Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256

Description:    The objective of this study was to investigate the inaccuracy of a capacitance moisture sensor mounted on a combine harvester based on the datasets of six consecutive years. Variation of sensed volume is a major cause of measurement error for a capacitive sensor. The percentage of the sensed volume occupied by grain changes continuously by filling and emptying of the grain bin, which causes a large fluctuation in sensor output during on-the-go moisture sensing. At the beginning of the bin filling process when the grain bin is empty, under-measures were recorded and when it is approximately 60 % full, large over-measures are observed compared to the actual moisture values. This effect mainly influences the precision of the recorded site-specific moisture values and causes inaccurate yield maps. To assess the effect of varying sensed volume content during harvest operation, a bin level transmitter sensor was mounted on the top of the grain bin to continuously measure the height of the grain. A clear correlation between the actual amount of material (available space) in the grain bin to the bias from the standard moisture was demonstrated. The coefficient of determination was R 2  = 0.86 for corn ( Zea mays L.) and R 2  = 0.87 for winter wheat ( Triticum aestivum L.). By using equations generated from the datasets of consecutive years (2008, 2009 and 2010), an effective post-correction method for the recorded data is proposed. Content Type Journal Article Pages 1-9 DOI 10.1007/s11119-012-9289-y Authors M. Csiba, Faculty of Agricultural and Food Sciences, Institute of Biosystems Engineering, University of West Hungary, Vár 2, 9200 Mosonmagyaróvár, Hungary A. J. Kovács, Faculty of Agricultural and Food Sciences, Institute of Biosystems Engineering, University of West Hungary, Vár 2, 9200 Mosonmagyaróvár, Hungary I. Virág, Faculty of Agricultural and Food Sciences, Institute of Biosystems Engineering, University of West Hungary, Vár 2, 9200 Mosonmagyaróvár, Hungary M. Neményi, Faculty of Agricultural and Food Sciences, Institute of Biosystems Engineering, University of West Hungary, Vár 2, 9200 Mosonmagyaróvár, Hungary Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256

Description:    Aerial images are useful tools for farmers who practise precision agriculture. The difficulty in taking geo-referenced high-resolution aerial images in a narrow time window considering weather restrictions and the high cost of commercial services are the main drawbacks of these techniques. In this paper, a useful tool to obtain aerial images by using low cost unmanned aerial vehicles (UAV) is presented. The proposed system allows farmers to easily define and execute an aerial image coverage mission by using geographic information system tools in order to obtain mosaics made of high-resolution images. The system computes a complete path for the UAV by taking into account the on-board camera features once the image requirements and area to be covered are defined. This work introduces a full four-step procedure: mission definition, automatic path planning, mission execution and mosaic generation. Content Type Journal Article Pages 1-18 DOI 10.1007/s11119-012-9287-0 Authors João Valente, Centre for Automation and Robotics (UPM-CSIC), c/José Gutiérrez Abascal, 2, 28006 Madrid, Spain David Sanz, Centre for Automation and Robotics (UPM-CSIC), c/José Gutiérrez Abascal, 2, 28006 Madrid, Spain Jaime Del Cerro, Centre for Automation and Robotics (UPM-CSIC), c/José Gutiérrez Abascal, 2, 28006 Madrid, Spain Antonio Barrientos, Centre for Automation and Robotics (UPM-CSIC), c/José Gutiérrez Abascal, 2, 28006 Madrid, Spain Miguel Ángel de Frutos, Centre for Automation and Robotics (UPM-CSIC), c/José Gutiérrez Abascal, 2, 28006 Madrid, Spain Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256

Description:    Signals for determining rates for site-specific nitrogen fertilisation can be obtained via different sensors. Optical systems that record the nitrogen supply status of the plant via the reflected sunlight have been widely validated. In particular, ground based systems like the YARA N-Sensor have been put into practice. However, such sensors have disadvantages as they only record a small part of the crop population to the left and right of the tramline. This disadvantage is overcome by data obtained from the air (aircraft) or space (satellite). In the study presented, three systems—ground, aerial and space—were compared; of particular interest were data from the RapidEye-System, which has delivered data since 2009. The comparison showed that if the (well suited) Red Edge Inflection Point (REIP) has to be calculated for the determination of the site-specific amount of N-fertiliser, then the system based on satellite imagery would not be suitable for determining N-rates. In addition, the delivered data were shifted by 35 m and had to be corrected. The aerial system also delivered spatially shifted data, however the REIP can be calculated without a problem. Upon considering the costs and the weather dependant availability of the data, the ground based system was most suitable, despite its disadvantage of providing an incomplete crop recording; the aerial system, however, provides a good alternative if its costs can be reduced. The space system would be a good alternative if it were able to deliver all four wavelength ranges that are necessary for the REIP. Content Type Journal Article Pages 1-16 DOI 10.1007/s11119-012-9278-1 Authors P. Wagner, Agribusiness and Farm Management Group, Martin-Luther-University Halle-Wittenberg, Karl-Freiherr-von-Fritsch-Straße 4, 06099 Halle, Germany K. Hank, Agribusiness and Farm Management Group, Martin-Luther-University Halle-Wittenberg, Karl-Freiherr-von-Fritsch-Straße 4, 06099 Halle, Germany Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256

Description:    The estimation of nitrogen concentration from remotely sensed data has been the subject of some work. However, few studies have addressed the effective model for monitoring nitrogen status at canopy level using Support Vector Machines (SVM). The present study is focused on the assessment of an estimation model for nitrogen concentration of rape canopy with hyperspectral data. Two types of estimation model, the traditional statistical method based on stepwise linear regression (SLR) and the emerging computationally powerful techniques based on support vector machines were applied The Root Mean Square Error (RMSE) and T values were used to assess their predictability. The results show that a better agreement between the observed and the predicted nitrogen concentration were obtained by using the SVM model. Compared to the SLR model, the SVM model improved the results by lowering RMSE by 11.86–21.13 %, and by increasing T by 20.00–29.41 % for different spectral transformations. The study demonstrated the potential of SVM to estimate nitrogen concentration using canopy level hyperspectral data and it was concluded that SVM may provide a useful exploratory and predictive tool when applied to canopy-level hyperspectral reflectance data for monitoring nitrogen status of rape. Content Type Journal Article Pages 1-12 DOI 10.1007/s11119-012-9285-2 Authors Fumin Wang, Institute of Hydrology and Water Resources, Zhejiang University, Zijingang Campus, Hangzhou, 310058 China Jingfeng Huang, Research Center of Agricultural Information Science & Technology, Zhejiang University, Huajiachi Campus, Hangzhou, 310029 China Yuan Wang, Research Center of Agricultural Information Science & Technology, Zhejiang University, Huajiachi Campus, Hangzhou, 310029 China Zhanyu Liu, Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou, 310036 China Fayao Zhang, Institute of Hydrology and Water Resources, Zhejiang University, Zijingang Campus, Hangzhou, 310058 China Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256

Description:    Variable-rate technology (VRT) has been used by farmers in an attempt to better match inputs to local growing conditions. In theory, VRT minimizes over- and under-application of inputs. However, the limitations and errors of this technology have not been well documented. Further, standard methods for quantifying the application accuracy using VRT do not currently exist, limiting practitioners’ knowledge on performance. Therefore, a spatial data model was developed to generate “as-applied” surfaces as a means to evaluate VRT performance of two applicators. The spatial data model uses geographic information system functionality to merge applicator descriptive patterns with a field application file to generate an ‘as-applied’ surface map representing not only the actual deposition of granular fertilizer but more importantly spatial distribution. Field data were collected and used to validate the spatial model. Comparisons between the actual and predicted application rates indicated moderate to good correlations (0.62 〈 R 〈 0.88) for two applicators. Longitudinal offset such as for a global positioning system receiver impacted model performance for one applicator but not the other. A comparison of the actual application rates to the prescription maps illustrated the inconsistency of VRT performance to deliver target rates. Both applicators were only within 10 % of the target rates a small percentage of time (〈45 %) during field operation. Generated as-applied surface maps highlighted errors associated with VRT along with limitations of the technology within site-specific management (SSM). Thus, as-applied surface maps provide a means to properly evaluate VRT while enhancing researchers’ and practitioners’ abilities to compare and customize SSM approaches. Content Type Journal Article Pages 1-17 DOI 10.1007/s11119-012-9286-1 Authors John P. Fulton, Biosystems Engineering Department, Auburn University, 200 Corley Building, Auburn, AL 36849, USA Scott A. Shearer, Department of Food, Agriculture and Biological Engineering, The Ohio State University, 590 Woody Hayes Dr., Columbus, OH 43210, USA Steve F. Higgins, Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA Timothy P. McDonald, Biosystems Engineering Department, Auburn University, 200 Corley Building, Auburn, AL 36849, USA Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256

Description:    Wheat aphid, Sitobion avenae F. is one of the most destructive insects infesting winter wheat and appears almost annually in northwest China. Past studies have demonstrated the potential of remote sensing for detecting crop diseases and insect damage. This study aimed to investigate the spectroscopic estimation of leaf aphid density by applying continuous wavelet analysis to the reflectance spectra (350–2 500 nm) of 60 winter wheat leaf samples. Continuous wavelet transform (CWT) was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength location and scale of decomposition. Linear regression between the wavelet power and aphid density was to identify wavelet features (coefficients) that might be the most sensitive to aphid density. The results identified five wavelet features between 350 and 2 500 nm that provided strong correlations with leaf aphid density. Spectral indices commonly used to monitor crop stresses were also employed to estimate aphid density. Multivariate linear regression models based on six sensitivity spectral indices or five wavelet features were established to estimate aphid density. The results showed that the model with five wavelet features (R 2  = 0.72, RMSE = 16.87) performed better than the model with six sensitivity spectral indices (R 2  = 0.56, RMSE = 21.19), suggesting that the spectral features extracted through CWT might potentially reflect aphid density. The results also provided a new method for estimating aphid density using remote sensing. Content Type Journal Article Pages 1-11 DOI 10.1007/s11119-012-9283-4 Authors Juhua Luo, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China Wenjiang Huang, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing, China Lin Yuan, Beijing Research Center for Information Technology in Agriculture, Beijing, China Chunjiang Zhao, Beijing Research Center for Information Technology in Agriculture, Beijing, China Shizhou Du, Institute of Crops Anhui Academy of Agricultural Sciences, Hefei, China Jingcheng Zhang, Beijing Research Center for Information Technology in Agriculture, Beijing, China Jinling Zhao, Beijing Research Center for Information Technology in Agriculture, Beijing, China Journal Precision Agriculture Online ISSN 1573-1618 Print ISSN 1385-2256