Soil heterogeneity at the field scale: a challenge for precision crop protection

Abstract

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,

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

“…Moreover, clay has the lowest share of the three texture compartments in our study area, such that it may have been less distinct within spectral reflectance. The SOC model provided results comparable to those achieved by Patzold et al [24] and Stevens et al [28]; indeed, it even slightly exceeded their quality.…”

“…Under laboratory conditions, Patzold et al [24] were able to predict SOC with PLSR and achieved an R 2 value of 0.93. They concluded that SOC estimations using laboratory spectroscopy lead to promising results.…”

Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy

“…SOC content, in particular, has been often related to reflectance data acquired by airborne spectrometers. with the HyMap sensor (420-2480 nm) while Patzold et al (2008) obtained a R 2 of 0.74 with the same sensor and a very limited set of composite soil samples. Less satisfactory prediction models of SOM were obtained by Bajwa and Tian (2005) with the RDACS/H-3 sensor (471-828 nm; R 2 = 0.66) and De Tar et al (2008) with the AVNIR sensor (429-1010 nm; R 2 = 0.48).…”

Improving the design of long-term monitoring experiments in forests: a new method for the assessment of local soil variability by combining infrared spectroscopy and dendrometric data

“…Mid-infrared spectra cover a larger range of frequencies and are more efficient to reflect the chemical properties of soil organic matter . They are preferentially used to predict organic components and exchangeable elements in litter and soil (Patzold et al 2008), especially soil carbon, nitrate, metals, and microelements (Kuang et al 2012), whereas near-infrared analyses associated with MIR reveal the physical properties of soil (Soriano-Disla et al 2014). Near-infrared spectra are a good predictor of clay content, exchangeable K (Viscarra Rossel et al 2006), and moisture content (Kuang et al 2012).…”