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Two groups of soil texture classification, differing in limit diameters for particular soil fractions, are used in Poland. The older groups of classifications consider soil particles as < 1 mm and divide them into sand (1.0–0.1 mm), silt (0.1–0.02 mm) and fine or flowable particles (< 0.02 mm). These classifications are used, in a version which originated in 1956 and here denominated as PTG/Musierowicz 1956 in land quality assessment and elaboration of soil agricultural maps. Newer versions of these classifications – professional standard BN-78/9180-11 (1978) and division of soils in agronomic categories described in 1986 – are used in fertilizer recommendations and in agricultural drought monitoring. According to the new soil texture classification PTG 2008, the upper diameter limit for soil particles is 2 mm and these parts are divided into sand (2.0–0.05 mm), silt (0.05–0.002 mm) and clay (<0.002 mm). This classification is compatible with soil texture classification elaborated by the United States Department of Agriculture (USDA) and most frequently applied worldwide. In this paper, the results of analyses of 1087 soil samples representing almost all granulometric groups existing in older and newer Polish soil texture classifications were considered. On the base of the current and earlier studies, the table with probability of occurrence of granulometric group PTG 2008 or texture class USDA for particular granulometric groups BN-78/9180-11 was prepared. For majority of granulometric groups (pl, ps, psp, pgl, pgmp, gp, gpp, gl, glp, gc, gcp, ip, płi) determined on the base of BN-78/9180-11, it is possible to determine their granulometric group/texture class according to PTG 2008/USDA correctly and, frequently, unambiguously. For granulometric groups pglp, pgm, gs, gsp i and płg it is possible to propose with great probability more than one equivalent according to PTG 2008/USDA, and in other cases more studies are recommended. [ABSTRACT FROM AUTHOR]

Active optical sensors (AOSs) are used for in-season variable-rate application of nitrogen (N). The sensors measure crop reflectance expressed as vegetative indices (VIs). These are transformed into N recommendations during onsite calibration of AOSs-'familiarising' the sensors with the crop N status of the representative part of a field. The 'drivefirst' method is often used by growers to calibrate AOSs. Due to large spatial variation of crop N status within fields, it is difficult to identify the most representative sample strip for AOS calibration. Seven site-years were used to evaluate the sensitivity of sensor-based N prescriptions for winter wheat (Triticum aestivum L.) to selection of sample strips for AOS calibration that fall into extreme, very low or very high values of 95th percentiles of amber normalised difference VI (NDVI) values. A Crop Circle ACS-210 sensor was used to collect canopy reflectance values, expressed as amber NDVI, at the beginning of wheat stem elongation. Our study showed that the sample-strip selection significantly affected sensor-based N prescriptions. The drive-first method may result in under- or over-applications ofNand in lower N-use efficiency. One way to overcome this problem is to collect whole field NDVI values during pesticide application before sensor-basedNapplication. The NDVI values from the entire field then can be used to choose the most representative sample strips for AOS calibration. [ABSTRACT FROM AUTHOR]

Agricultural soil maps were created to facilitate the management of fields. Such maps, at very large scale (1:5000), cover almost the entire agricultural land in Poland. These maps can be very useful for precision field management. However, they were prepared about 40-50 years ago using old mapping techniques with mainly field (organoleptic) examination of soil. For this reason it is necessary to verify agreement between such maps with the current soil status. In the case of detection of disagreements it is important to identify the causes and propose methods for improving soil maps. Only few studies were made in this aspect. The soil texture (ST) of the upper layer, as presented on the agricultural soil maps from the 1960s and 1970s, was compared with the actual ST of the same four fields in northern and central Poland. For laboratory ST determination, soil samples were collected in a dense sampling network. On average, the agreement of the ST presented on the agricultural soil maps and determined in this study was classified as medium for three fields and good for one field. However, the presence of field areas with poor agreement between the ST determined in this study and that shown on the agricultural maps was detected in all investigated fields. Identified causes for this comprised imprecision of ST determination using the organoleptic method, the generalization of the soil quality maps, and erosion processes during the years between soil mapping and this investigation. The improvement of ST presentation on the large scale soil maps might be achieved by quite denser soil sampling for ST analysis supported by ECa, yield and NDVI maps. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The proper delineation of management zones (MZs) is very important in site-specific crop management. The aim of this study was to determine the soil attributes that are most relevant in the delineation process of MZs. Two fields of 22 ha and 45 ha located in northern Poland were examined. The analyses were based on MZs delineated using standardized multiyear yield data and logistic regression, which was applied to select the variables that discriminate the yield-based MZs to the greatest degree. The effects of several variables, including soil chemical and physical properties and topographic attributes, on the discrimination of yield-based MZs were investigated. The sand and organic carbon content in the soil produced the most relevant delineation of MZs. Highly correlated variables should be not included in analyses for MZ delineation because they can strongly affect the results of cluster analyses that are used for the delineation of MZs. [ABSTRACT FROM PUBLISHER]