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| Filter results7 paper(s) found. |
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1. A Statistical and an Agronomic Approach for Definition of Management Zones in Corn and SoybeanThe use of productivity level management zones (MZ) has demonstrated good potential for the site-specific management of crop inputs in traditional row crops. The objectives of this research were to analyze the process of defining MZs and develop methods to evaluate the quality of MZ maps. Two approaches were used to select the layers to be used in the MZ definition: 1) Statistical Approach (SA_MZ) and 2) Agronomic Approach (AA_MZ). The difference is that in the AA_MZ approach all non stable variables... C.L. Bazzi, E.G. Souza, R. Khosla, R.M. Reich |
2. Use of Chemical and Physical Attributes Of the Soil in Management Units DefinitionSeveral equipments and methodologies have been developed to make available precision agriculture, especially the high cost of its implantation and sampling. An interesting possibility... C.L. Bazzi, E.G. Souza, L.H. Nobrega, M.A. Uribe-opazo, D.M. Rocha |
3. Smoothness Index Of Thematic MapsA thematic map shows the spatial distribution of one or more specific data themes for standard geographic areas. The thematic maps are generated to represent the studied variables, so interpolators are used to determine their values in places not sampled. It is usually... C.L. Bazzi, E.G. Souza, D. Stiehl |
4. Thematic And Profitability Maps For Precision AgricultureYield maps became economically feasible to farmers with the technological advances in precision agriculture. The evidence of its profitability, however, is still unknown and, rarely, yield variability has been correlated to profitable variability. Differently from... E.G. Souza, C.L. Bazzi, M.A. Uribe-opazo |
5. Climate Sensitivity Analysis on Maize Yield on the Basis of Precision Crop ProductionIn this paper by prediction we have defined maize yield in precision plant production technologies according to five different climate change scenarios (Ensembles Project) until 2100 and in one scenario until 2075 using DSSAT v. 4.5.0. CERES-Maize decision support model. Sensitivity analyses were carried out. The novelty of the method presented here is that precision, variable rate technologies from relatively small areas (in our case 2500 m2) enable a large amount of data to be collected... A. Nyeki, G. Milics, A.J. Kovacs, M. Neményi, J. Kalmar |
6. Sampling Bumble Bees and Floral Resources Using Deep Learning and UAV ImageryPollinators, essential components of natural and agricultural systems, forage over relatively large spatial scales. This is especially true of large generalist species, like bumble bees. Thus, it can be difficult to estimate the amount and diversity of floral resources available to them. Floral cover and diversity are often estimated over large areas by extrapolation from small scale samples (e.g., a 1-m quadrat) but the accuracy of such estimates can vary depending on the spatial patchiness of... B. Spiesman, I. Grijalva, D. Holthaus, B. Mccornack |
7. Detection of Sorghum Aphids with Advanced Machine VisionSorghum aphid, Melanaphis sorghi (Theobald), became a significant pest concern due to the significant yield losses caused in the sorghum production region. Different management practices, including monitoring and applying insecticides, have been used to manage this invasive pest in sorghum. The most common management strategy consists of visual assessments of aphids on sorghum leaves to determine an economic threshold level to spray. However, because of their rapid reproduction,... I.A. Grijalva teran, B. Spiesman, N. Clark, B. Mccornack |