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Crop protection seldom takes into account soil heterogeneity at the field scale. Yet, variable site characteristics affect the incidence of pests as well as the efficacy and fate of pesticides in soil. This article reviews crucial starting points for incorporating soil information into precision crop protection (PCP). At present, the lack of adequate field maps is a major drawback. Conventional soil analyses are too expensive to capture soil heterogeneity at the field scale with the required spatial resolution. Therefore, we discuss alternative procedures exemplified by our own results concerning (i) minimally and noninvasive sensor techniques for the estimation of soil properties, (ii) the evidence of soil heterogeneity with respect to PCP, and (iii) current possibilities for incorporation of high resolution soil information into crop protection decisions. Soil organic carbon (SOC) and soil texture are extremely interesting for PCP. Their determination with minimally invasive techniques requires the sampling of soils, because the sensors must be used in the laboratory. However, this technique delivers precise information at low cost. We accurately determined SOC in the near-infrared. In the mid-infrared, texture and lime content were also exactly quantified. Non-invasive sensors require less effort. The airborne HyMap sensor was suitable for the detection of variability in SOC at high resolution, thus promising further progress regarding SOC data acquisition from bare soil. The apparent electrical conductivity as measured by an EM38 sensor was shown to be a suitable proxy for soil texture and layering. A survey of arable fields near Bonn (Germany) revealed widespread within-field heterogeneity of texture-related ECa, SOC and other characteristics. Maps of herbicide sorption and application rate were derived from sensor data,

Modeling global C cycles requires in‐depth knowledge about small‐scale C stocks and turnover processes, yet different soil organic C (SOC) pools reveal considerable spatiotemporal heterogeneity at the field scale, which is scarcely known due to the considerable workload associated with traditional fractionation procedures. We investigated the potential of mid‐infrared spectroscopy combined with partial least squares regression (MIRS‐PLSR) for rapid assessment of different particulate organic matter (POM) pools and their spatial heterogeneity at the field scale. Locally calibrated prediction models estimated the contents of SOC, POM of three size classes (POM1: 2000–250 μm; POM2: 250–53 μm; and POM3: 53–20 μm), and lignin contents for 129 locations in a 1.3‐ha test field. Relations between the parameters were described using correlation analysis and fuzzy‐ κ statistics. All parameters were predicted successfully by applying local calibrations for MIRS‐PLSR (R2 = 0.77–0.96). The prediction model for POM1 chiefly relied on specific signals of lignin and cellulose; contents of POM2 were estimated by spectral bands assigned to degradation products as aliphatic C–H groups and aromatic moieties; carboxylic groups essentially contributed to the prediction of POM3. There was a close spatial relation between the coarse POM1 and POM2 fractions and lignin (κ = 0.77), which largely also explained variations in bulk SOC. In contrast, POM3 exhibited a less deterministic pattern in the field, thus contributing little to the spatial variation in SOC content.

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