Precision Agriculture (Journal)

Abstract  A recent paper in Precision Agriculture concluded that algorithms to calculate in-season fertilizer nitrogen (N) recommendations need to include yield and fertilizer response considerations because grain yield and yield response index are independent of each other. The authors used maximum and zero N yields from selected long-term wheat and maize studies to support their conclusion. Yields from plots receiving intermediate N rates in the maize study indicated a probable dependence between grain yield and yield response index, which is contrary to the authors’ conclusions. Data from a more recent, long-term irrigated maize study on a similar soil type were used to illustrate that grain yield and yield response index are definitely dependent on each other and further that the in-season sensor-based sufficiency index is highly correlated with relative yield. The implication is that a yield component, as such, does not necessarily need to be included in development of an in-season N recommendation algorithm.

• Content Type Journal Article
• Category SHORT DISCUSSION
• Pages 1-9
• DOI 10.1007/s11119-012-9258-5
• Authors
  • James S. Schepers, USDA-ARS Collaborator, 3820 Loveland Dr., Lincoln, NE 68506, USA
  • Kyle H. Holland, Holland Scientific, 6001 S. 58th Street, Suite D, Lincoln, NE 68516, USA

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

Abstract  An unmanned aerial vehicle (“VIPtero”) was assembled and tested with the aim of developing a flexible and powerful tool for site-specific vineyard management. The system comprised a six-rotor aerial platform capable of flying autonomously to a predetermined point in space, and of a pitch and roll compensated multi-spectral camera for vegetation canopy reflectance recording. Before the flight campaign, the camera accuracy was evaluated against high resolution ground-based measurements, made with a field spectrometer. Then, “VIPtero” performed the flight in an experimental vineyard in Central Italy, acquiring 63 multi-spectral images during 10 min of flight completed almost autonomously. Images were analysed and classified vigour maps were produced based on normalized difference vegetation index. The resulting vigour maps showed clearly crop heterogeneity conditions, in good agreement with ground-based observations. The system provided very promising results that encourage its development as a tool for precision agriculture application in small crops.

• Content Type Journal Article
• Category Technical Note
• Pages 1-7
• DOI 10.1007/s11119-012-9257-6
• Authors
  • Jacopo Primicerio, IBIMET-CNR, Istituto di Biometeorologia—Consiglio Nazionale delle Ricerche, Via Giovanni Caproni, 8, 50145 Firenze, Italy
  • Salvatore Filippo Di Gennaro, IBIMET-CNR, Istituto di Biometeorologia—Consiglio Nazionale delle Ricerche, Via Giovanni Caproni, 8, 50145 Firenze, Italy
  • Edoardo Fiorillo, IBIMET-CNR, Istituto di Biometeorologia—Consiglio Nazionale delle Ricerche, Via Giovanni Caproni, 8, 50145 Firenze, Italy
  • Lorenzo Genesio, IBIMET-CNR, Istituto di Biometeorologia—Consiglio Nazionale delle Ricerche, Via Giovanni Caproni, 8, 50145 Firenze, Italy
  • Emanuele Lugato, IBIMET-CNR, Istituto di Biometeorologia—Consiglio Nazionale delle Ricerche, Via Giovanni Caproni, 8, 50145 Firenze, Italy
  • Alessandro Matese, IBIMET-CNR, Istituto di Biometeorologia—Consiglio Nazionale delle Ricerche, Via Giovanni Caproni, 8, 50145 Firenze, Italy
  • Francesco Primo Vaccari, IBIMET-CNR, Istituto di Biometeorologia—Consiglio Nazionale delle Ricerche, Via Giovanni Caproni, 8, 50145 Firenze, Italy

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

Abstract  Automatic section control was analyzed in a whole farm decision-making framework when implemented on an agricultural sprayer and/or planter. In addition, various field types and navigational scenarios were examined to determine their impact on profitability. It was determined that automatic section control increased net returns under all scenarios; up to $36/ha. This investigation highlighted the importance of considering field size in addition to field shape as well as initial navigational scenarios when determining the profitability of automatic section control.

• Content Type Journal Article
• Pages 1-10
• DOI 10.1007/s11119-011-9256-z
• Authors
  • Jordan Shockley, Department of Agricultural Economics, University of Kentucky, 331 C.E. Barnhart Building, Lexington, KY 40546-0276, USA
  • Carl R. Dillon, Department of Agricultural Economics, University of Kentucky, 403 C.E. Barnhart Building, Lexington, KY 40546-0276, USA
  • Tim Stombaugh, Department of Biosystems and Agricultural Engineering, University of Kentucky, 214 C.E. Barnhart Building, Lexington, 40546-0276 KY, USA
  • Scott Shearer, Department of Food, Agriculture and Biological Engineering, The Ohio State University, 590 Woody Hayes Dr, Columbus, 43210 OH, USA

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

Editorial for special issue of papers from the 10th International Conference on Precision Agriculture (ICPA)

• Content Type Journal Article
• Category Editorial
• Pages 1-1
• DOI 10.1007/s11119-011-9255-0
• Authors
  • James Schepers, USDA-ARS, University of Nebraska, Lincoln, NE, USA

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

Abstract  Knowledge of spatial variability of soil fertility and plant nutrition is critical for planning and implementing site-specific vineyard management. To better understand the key drivers behind vineyard variability, yield mapping from 2002 to 2005 and 2007 (the monitor broke down in 2006) was used to identify zones of different productive potential in a Pinot Noir field located in Raimat (Lleida, Spain). Simultaneously, the vineyard field was sampled in 2002, 2003 and 2007, applying three different schemes (depending on the number of target vines in different grape yield zones). The sampling carried out in 2002, which involved different soil, topographic and crop properties (mineral contents in petiole), made it possible to evaluate the influence of these parameters on the grape yield variability. The zones of lowest yield coincided with locations in which the nutritional status of the crop exhibited the lowest values, particularly with respect to petiole contents of calcium and manganese. Sampling systems adopted in 2003 and 2007 (grape quality and soil attributes) confirmed the inverse spatial correlation between grape yield and some grape quality parameters and, more importantly, showed that the percentage of soil carbonates had a great influence on grape quality probably due to the reduced availability of manganese in calcareous soils. Site-specific vineyard management could therefore be considered using two different strategies: variable-rate application of foliar fertilizers to increase the yield in areas with low production and also foliar or soil fertilizers to improve the quality specifications in some areas.

• Content Type Journal Article
• Pages 1-18
• DOI 10.1007/s11119-011-9254-1
• Authors
  • J. Arnó, Department of Agro-forestry Engineering, University of Lleida, Rovira Roure, 191, 25198 Lleida, Spain
  • J. R. Rosell, Department of Agro-forestry Engineering, University of Lleida, Rovira Roure, 191, 25198 Lleida, Spain
  • R. Blanco, Department of Crop and Forest Science, University of Lleida, Rovira Roure, 191, 25198 Lleida, Spain
  • M. C. Ramos, Department of Environment and Soil Sciences, University of Lleida, Rovira Roure, 191, 25198 Lleida, Spain
  • J. A. Martínez-Casasnovas, Department of Environment and Soil Sciences, University of Lleida, Rovira Roure, 191, 25198 Lleida, Spain

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

Abstract  Due to economical and ecological reasons it is important to provide the necessary flexibility in fertilizer management to respond to differences in plant nutrients requirements. Plant-based sensors have potential to provide more accurate and on-line information regarding crop bio-responses to environmental stress and could overcome limitations of traditional methods which focus only on monitoring parameters of soil. Current research regarding on-line plant stress sensing techniques concentrates on spectroscopic and image processing methods. These techniques have many limitations connected with their sensitivity to environmental interferences. In recent years, impedance spectroscopy has become a well-known non-invasive tool for describing the electrical properties of many systems. The research hypothesis tested was that information provided by Electrical Impedance Spectroscopy is correlated with tomato plant stress caused by lack of mineral nutrients in the growth medium. The experiment was conducted with two sets of hydroponically-grown tomato plants (Lycopersicon esculentum Mill., cv. ‘Maliniak’). During the experiment the tomato plants were fed alternately with flow of necessary nutrients and with distilled water. The impedance spectra were measured by scanning frequencies from 100 Hz to 50 kHz to determine the most sensitive frequency. A Nutrition Index was proposed for indicating variability of mineral nutrition within plants, and its correlation with experimental plant data was tested. Data showed that the relation between the Nutrition Index and the stress caused by lack of mineral nutrients in the growing medium was a monotonic function in the case of study. The results presented in the paper support the concept that the electrical impedance spectroscopy is a non-destructive, economical and reliable measurement method, which can be utilised for plant nutrition stress monitoring.

• Content Type Journal Article
• Pages 1-14
• DOI 10.1007/s11119-011-9252-3
• Authors
  • Dariusz Tomkiewicz, Division of Control Engineering, Koszalin University of Technology, ul. Raclawicka 15, 75-620 Koszalin, Poland
  • Tomasz Piskier, Division of Agricultural Engineering, Koszalin University of Technology, ul. Raclawicka 15, 75-620 Koszalin, Poland

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

Abstract  Every growing season, paddy fields are kept both flooded and drained for a significant period of time. As a consequence, these soils develop distinct physico-chemical characteristics. For practical reasons, these soils are mostly sampled under dry conditions, but the question arises how representative the results are for the wet growing conditions. Therefore, the apparent electrical conductivity (ECa) of a 1.4 ha alluvial paddy field located in the Brahmaputra floodplain of Bangladesh was measured in both dry and wet conditions by a sensing system using the electromagnetic induction sensor EM38, which does not require physical contact with the soil, and compared both surveys. Due to the smooth water surface under wet conditions which ensured increased stability of the sensing platform, the results of the survey showed considerably reduced micro-scale variability of ECa. Furthermore, the wet survey results more reliably furnished soil-related information mainly due to the absence of soil moisture dynamics. The differences between ECa under wet and dry conditions were attributed to differences in soil texture, mainly the sand content variation having considerable effect on soil moisture differences when flooded following drainage. Accordingly, the largest differences between ECa under wet and dry conditions were found in those parts of the field with a large sand content. Hence, the conclusion was that an ECa survey on flooded fields has an added value to precision soil management.

• Content Type Journal Article
• Pages 1-9
• DOI 10.1007/s11119-011-9253-2
• Authors
  • Mohammad Monirul Islam, Research Group Soil Spatial Inventory Techniques, Department of Soil Management, Faculty of Bioscience Engineering, Ghent University, Coupure 653, 9000 Ghent, Belgium
  • Eef Meerschman, Research Group Soil Spatial Inventory Techniques, Department of Soil Management, Faculty of Bioscience Engineering, Ghent University, Coupure 653, 9000 Ghent, Belgium
  • Timothy Saey, Research Group Soil Spatial Inventory Techniques, Department of Soil Management, Faculty of Bioscience Engineering, Ghent University, Coupure 653, 9000 Ghent, Belgium
  • Philippe De Smedt, Research Group Soil Spatial Inventory Techniques, Department of Soil Management, Faculty of Bioscience Engineering, Ghent University, Coupure 653, 9000 Ghent, Belgium
  • Ellen Van De Vijver, Research Group Soil Spatial Inventory Techniques, Department of Soil Management, Faculty of Bioscience Engineering, Ghent University, Coupure 653, 9000 Ghent, Belgium
  • Marc Van Meirvenne, Research Group Soil Spatial Inventory Techniques, Department of Soil Management, Faculty of Bioscience Engineering, Ghent University, Coupure 653, 9000 Ghent, Belgium

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

Abstract  A study was conducted to explore the potential use of a hand-held (proximal) hyperspectral sensor equipped with a canopy pasture probe to assess a number of pasture quality parameters: crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, dietary cation–anion difference (DCAD), lignin, lipid, metabolisable energy (ME) and organic matter digestibility (OMD) during the autumn season 2009. Partial least squares regression was used to develop a relationship between each of these pasture quality parameters and spectral reflectance acquired in the 500–2 400 nm range. Overall, satisfactory results were produced with high coefficients of determination (R 2), Nash–Sutcliffe efficiency (NSE) and ratio prediction to deviation (RPD). High accuracy (low root mean square error-RMSE values) for pasture quality parameters such as CP, ADF, NDF, ash, DCAD, lignin, ME and OMD was achieved; although lipid was poorly predicted. These results suggest that in situ canopy reflectance can be used to predict the pasture quality in a timely fashion so as to assist farmers in their decision making.

• Content Type Journal Article
• Pages 1-19
• DOI 10.1007/s11119-011-9251-4
• Authors
  • R. R. Pullanagari, New Zealand Centre for Precision Agriculture (NZCPA), Institute of Natural Resources, Massey University, Private Bag 11-222, Palmerston North, New Zealand
  • I. J. Yule, New Zealand Centre for Precision Agriculture (NZCPA), Institute of Natural Resources, Massey University, Private Bag 11-222, Palmerston North, New Zealand
  • M. P. Tuohy, New Zealand Centre for Precision Agriculture (NZCPA), Institute of Natural Resources, Massey University, Private Bag 11-222, Palmerston North, New Zealand
  • M. J. Hedley, Institute of Natural Resources, Massey University, Private Bag 11-222, Palmerston North, New Zealand
  • R. A. Dynes, AgResearch, Lincoln Research Centre, Christchurch, 8140 New Zealand
  • W. M. King, AgResearch, Ruakura Research Centre, Hamilton, 3240 New Zealand

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

Abstract  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 >500 m2 and 55.0% of all patches were smaller than 10 m2. 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 equipment.

• Content Type Journal Article
• Pages 1-15
• DOI 10.1007/s11119-011-9249-y
• Authors
  • David Gómez-Candón, Institute for Sustainable Agriculture, CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Francisca López-Granados, Institute for Sustainable Agriculture, CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Juan J. Caballero-Novella, Institute for Sustainable Agriculture, CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Alfonso García-Ferrer, Faculty of Agriculture and Forestry, University of Cordoba, Campus of Rabanales, Cordoba, Spain
  • José M. Peña-Barragán, Institute for Sustainable Agriculture, CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Montserrat Jurado-Expósito, Institute for Sustainable Agriculture, CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Luis García-Torres, Institute for Sustainable Agriculture, CSIC, Apartado 4084, 14080 Cordoba, Spain

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

Abstract   Avena sterilis weed pressure categories can be discriminated in wheat through remote images taken at late stages of wheat senescence. Site-specific image processing was achieved with SARI®, an add-on software program for ENVI® developed to implement precision agriculture. Using the SARI software and crop-weed competition and economic models, the precision yield losses for each micro-plot can be estimated and herbicide prescription maps obtained. Simulation studies on control indicators and herbicide use efficiency were undertaken using real-time ground data and remote images of two wheat plots infested with Avena sterilis at LaFloridaII and Navajas (Southern Spain). The simulation indicated that precision application of herbicides would produce higher overall herbicide savings (OHS) compared to broadcast applications but would vary depending on the level of weed infestation, the decision making criteria (DMC) of applying herbicide above a weed infestation level and the size of the spray grid considered. For example, for areas with low levels of infestation (around 15%), the OHS was 20, 44, 81 and 90% for a DMC of 0, 10, 20 and 30%, respectively. SARI® also estimates the overall herbicide application efficiency (OHAE), a key agro-environmental index to estimate the efficiency of herbicide applications in weedy areas and the lack of herbicide applications in weed-free areas. Ideally, the OHAE is equal to 1 if weed control is complete and herbicide applications in weed-free areas are not necessary. The OHAE index is influenced by the size of the micro-plot and decision-making herbicide application criteria (DMC). The OHAE values increased as the size of the micro-plot decreased, regardless of the intensity of the weed infestation. For example, micro-plots of 20 × 6 m, 5 × 3 m and 1.2 × 1.5 m had OHAE values of 0.27, 0.57 and 0.76, for areas of low infestation, when averaged over the DMC. Generally, the OHAE values increased as the size of the micro-plot decreased regardless of the intensity of weed infestation. Based on actual weed abundance data, competition models and production costs, SARI® estimated wheat yield losses and economic net return for each micro-plot and herbicide application strategy. In both locations, weed infestation varied spatially from virtually weed-free micro-plots to 15 and 24% winter wheat yield loss in Navajas and LaFloridaII, respectively. Preliminary calculations indicate that net returns were slightly higher for areas with site-specific adjusted-rate applications than for the overall standard label rate application strategy. Both of these strategies provided considerably higher net returns compared to non-treated areas.

• Content Type Journal Article
• Pages 1-14
• DOI 10.1007/s11119-011-9250-5
• Authors
  • David Gómez-Candón, Institute for Sustainable Agriculture CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Francisca López-Granados, Institute for Sustainable Agriculture CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Juan J. Caballero-Novella, Institute for Sustainable Agriculture CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Alfonso García-Ferrer, Faculty of Agriculture and Forestry, Campus of Rabanales, University of Cordoba, Cordoba, Spain
  • José M. Peña-Barragán, Institute for Sustainable Agriculture CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Montserrat Jurado-Expósito, Institute for Sustainable Agriculture CSIC, Apartado 4084, 14080 Cordoba, Spain
  • Luis García-Torres, Institute for Sustainable Agriculture CSIC, Apartado 4084, 14080 Cordoba, Spain

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

Abstract  Cruciferous weeds are competitive broad-leaved species that cause losses in winter crops. In the present study, research on remote sensing was conducted on seven naturally infested fields located in Córdoba and Seville, southern Spain. Multi-spectral aerial images (four bands, including blue (B), green (G), red (R) and near-infrared bands) taken in April 2007 were used to evaluate the feasibility of mapping cruciferous patches (Diplotaxis spp. and Sinapis spp.) in winter crops (wheat, broad bean and pea) and compare the accuracy of different supervised classification methods (vegetation indices, maximum likelihood and spectral angle mapper). The best classification method was selected to develop site-specific cruciferous treatment maps. Cruciferous patches were efficiently discriminated with red/blue (R/B) and blue/green (B/G) vegetation indices and the maximum likelihood classifier. At all of the locations, the accuracy of the results obtained from the spectral angler mapper was relatively low. The cruciferous weed-classified imagery of each location were created according to the method that provided the best discrimination results and were used to obtain site-specific treatment maps for in-season post-emergence control measures or herbicide applications for subsequent years. By applying the site-specific treatment maps, herbicide savings from 71.7 to 95.4% for the no-treatment areas and from 4.3 to 12% for the low-dose herbicide were obtained.

• Content Type Journal Article
• Pages 1-20
• DOI 10.1007/s11119-011-9247-0
• Authors
  • Ana Isabel de Castro, Institute for Sustainable Agriculture IAS/CSIC, P.O. Box 4084, 14080 Córdoba, Spain
  • Montserrat Jurado-Expósito, Institute for Sustainable Agriculture IAS/CSIC, P.O. Box 4084, 14080 Córdoba, Spain
  • José M. Peña-Barragán, Institute for Sustainable Agriculture IAS/CSIC, P.O. Box 4084, 14080 Córdoba, Spain
  • Francisca López-Granados, Institute for Sustainable Agriculture IAS/CSIC, P.O. Box 4084, 14080 Córdoba, Spain

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

Abstract  Vegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance (crop fractional cover estimated with spectral unmixing) derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band and three-band VIs based on selected wavelengths for quantifying crop yield variability. Airborne 102-band hyperspectral images acquired at the peak development stage and yield monitor data collected from two grain sorghum fields were used. A total of 64 VI images were generated based on the eight VIs and selected wavelengths for each field in this study. Two spectral distance images, two spectral angle images and two abundance images were also created based on a pair of pure plant and soil reference spectra for each field. Correlation analysis with yield showed that the eight VIs with the selected wavelengths had r values of 0.73–0.79 for field 1 and 0.82–0.86 for field 2. Although all VIs provided similar correlations with yield, the modified soil-adjusted vegetation index (MSAVI) produced more consistent r values (0.77–0.79 for field 1 and 0.85–0.86 for field 2) among the selected bands. Spectral distance, spectral angle and plant abundance produced similar r values (0.76–0.78 for field 1 and 0.83–0.85 for field 2) to the best VIs. The results from this study suggest that either a VI (MSAVI) image based on one near-infrared band (800 or 825 nm) and one visible band (550 or 670 nm) or a plant abundance image based on a pair of pure plant and soil spectra can be used to estimate relative yield variation from a hyperspectral image.

• Content Type Journal Article
• Pages 1-14
• DOI 10.1007/s11119-011-9248-z
• Authors
  • Chenghai Yang, USDA-ARS Kika de la Garza Subtropical Agricultural Research Center, 2413 E. Highway 83, Weslaco, TX 78596, USA
  • James H. Everitt, USDA-ARS Kika de la Garza Subtropical Agricultural Research Center, 2413 E. Highway 83, Weslaco, TX 78596, USA

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