Improvements of nitrogen use effi ciency (NUE) may be achieved through the use of sensing tools for N status determination. Leaf and canopy chlorophyll, as well as leaf polyphenolics concentrations, are characteristics strongly aff ected by N availability that are oft en used as a surrogate to direct plant N status estimation. Approaches with near-term operational sensors, handheld and tractor-mounted, for proximal remote measurements are considered in this review. However, the information provided by these tools is unfortunately biased by factors other than N. To overcome this obstacle, normalization procedures such as the well-fertilized reference plot, the no-N reference plot, and relative yield are oft en used. Methods to establish useful relationships between sensor readings and optimal N rates, such as critical NSI (nitrogen suffi ciency index), INSEY (in-season estimated yield), and the relationship between chlorophyll meter readings, grain yield, and sensor-determined CI (chlorophyll index) are also reviewed. In a few cases, algorithms for translating readings into actual N fertilizer recommendation have been developed, but their value still seems limited to conditions similar to the ones where the research was conducted. Near-term operational sensing can benefi t from improvements in sensor operational characteristics (size and shape of footprint, positioning) or the choice of light wavebands more suitable for specifi c conditions (i.e., genotype, growth stage, or crop density). However, one important limitation to their widespread use is the availability of algorithms that would be reliable in a variety of soil and weather conditions.

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Active optical sensors (AOSs) measure crop refl ectance at specifi c wavelengths and calculate vegetation indices (VIs) that are used to prescribe variable N fertilization. Visual observations of winter wheat (Triticum aestivum L.) plant greenness and density suggest that VI values may be genotype specifi c. Some sensor systems use correction coeffi cients to eliminate the eff ect of genotype on VI values. Th is study was conducted to assess the eff ects of winter wheat cultivars and growing conditions on canopy refl ectance, as measured by red or amber normalized diff erence vegetative indices (NDVIs) derived from AOSs. Variations in NDVI values among three wheat cultivars were measured at three growth stages (Zadoks 31, 37, and 65) during 3 yr at three sites in Poland. GreenSeeker Model 505 and Crop Circle ACS-210 sensors were utilized to measure red and amber NDVIs, respectively. Signifi cant (p < 0.05) diff erences in both forms of NDVI associated with wheat genotypes were observed across years and sites at Zadoks 31, the time when canopy sensing and N fertilization decisions are oft en made. Lack of a genotype ´ site interaction for both red and amber NDVIs and the presence of a signifi cant genotype ´ year interaction for both VIs suggested that (i) canopy greenness and density of the same genotype measured at the same growth stage are likely to be stable across diff erent growing conditions, and (ii) NDVI values for a particular genotype tend to vary more across years than across sites. Because developing temporally variable correction coeffi cients is not practical, we strongly recommend that an in situ calibration (based on in-fi eld or a virtual reference strip) is utilized to normalize NDVI across genotypes, years, and sites.

Soil testing is used to help make fertilizer recommendations for greater yields and profits. But the increase of soil-sampling density raises costs of sample collection and analyses. The aim of this study was to compare grid-cell sampling densities (1, 2, and 4 ha) in terms of the estimation accuracy of macronutrients (P, K, Mg) availability and pH and to investigate how sampling density affects the amount of fertilizers and lime recommended and correctly applied to winter wheat (Triticum aestivum L.). The distribution of liming requirements and available nutrients were quite similar for the 1-and 2-ha grids but notably different for the 4-ha grid. However, the whole-field average values of pH and P, K, and Mg concentrations in soil obtained for different sampling densities were very similar, thus placing, respectively, the soil of the studied area in the same class of liming needs and nutrient availability. The range and estimation errors of these parameters decreased with sampling-grid size increase. The amount of lime and fertilizers to be applied on the field and the portion of a field correctly limed or fertilized depended on the soil chemical property considered. If one treats the 1-ha grid as the reference and the most correct soil-sampling approach, 2-ha grid offered the greatest part of the field to be adequately fertilized with lime, P, and K. However, fertilization with Mg was much more appropriate if the recommendation was based on 4-ha, than on a 2-ha soil-sampling grid. To gain an insight into soil variation and soil process occurring at small scale, laboratory and geostatistical analyses on individual soil samples may be necessary in some cases. Possibly, such costly research can deliver relevant information which could be then applied into farmer's practice.

Soil texture (ST) is relatively stable over time, although it may change due to erosion, clay eluviation, and other processes. Soil texture affects soil quality, productivity and management. Therefore, indirect, accurate methods for assessing of soil texture classes (STCs) are needed in agricultural practice. A study was performed on four production fields in northern and central Poland to compare the fitting performance of STC models based on apparent electrical conductivity (ECa), topographic properties (elevation, slope gradient and wetness index) and Amber NDVI measurements. One common and accurate indicator of STCs was not found for all study fields. On average, ECa was most accurate in indicating areas of different STCs within the fields, but it tended to overestimate the size of sandy areas on loamy fields and vice versa. The accuracy of STC assessment using ECa measurements may be biased due to imperfect soil drainage, high elevations, which increase evaporation and STC variation with depth. STC assessment using Amber NDVI measurements may be useful, particularly on flat and sandy fields, but the results are affected by the same factors as ECa, and additionally by crop growth stages and by the weather conditions in the period preceding the measurements. Despite the good quantitative results of the STC assessment by elevation (one field) and by the topographic wetness index (another field), both terrain attributes failed to accurately indicate the distribution of some STC areas within each field. Therefore, in landscapes developed from deposits of the last glaciation relevant ST differences might not sufficiently be detected by the analysis of terrain attributes alone. The selection of STC predictors and evaluation of the assessment quality must consider both the quantitative indicators such as correlation and determination coefficients describing relationships between ST and ECa, NDVI and topography and percentage of a field area with accurately indicated STC and the distribution of areas with different STCs within a field. The use of ECa, NDVI values, and topographic properties for STC assessment may be useful in reducing costs of soil sampling and analysis, but cannot replace it.

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Soil texture was examined in four crop fields with areas of 10 to 45 ha located in northern and central Poland. In each field, from 21 to 60 soil samples were collected using stratified sampling. The content (%) of soil particles, i.e., sand, silt and clay, was then evaluated using laboratory methods. The apparent electrical conductivity (ECa) was measured and used as ancillary data for the interpolation of soil texture. The obtained data were used to compare selected spatial interpolation methods according to the accuracy of prediction. The examined methods were evaluated based on the results of cross-validation tests. Two methods of validation were used: leave-oneout cross-validation and validation based on a test set of points, with approximately 30% randomly selected. The smallest root mean square error (RMSE) for the prediction of sand, silt and clay was observed for ordinary cokriging in which ECa was used as a covariate. The other three methods, i.e., inverse distance weighting, radial basis functioning and ordinary kriging, had very similar RMSE values, which were approximately 10% higher compared to ordinary cokriging.