Empirical predictions of plant material C and N mineralization patterns from near infrared spectroscopy, stepwise chemical digestion and C/N ratios

Prediction of carbon (C) and nitrogen (N) mineralization patterns of plant litter is desirable for both agronomic and environmental reasons. Near infrared reflectance (NIR) spectroscopy has recently been introduced in decomposition studies to characterize biochemical composition. The purpose of the current study was to use empirical techniques to predict C and N mineralization patterns of a wide range of plant materials incubated under controlled temperature and moisture conditions. We hypothesized that the richness of information in the NIR spectra would considerably improve predictions compared to traditional stepwise chemical digestion (SCD) or C/N ratios. Initially, we fitted a number of empirical functions to the observed C and N mineralization patterns. The best functions fitted with R²=0.990 and 0.949 to C and N, respectively. The fractions of C and N mineralized at different points in time were then either predicted directly with regression functions or indirectly by prediction of the parameters of the empirical functions fitted to incubation data. In both cases, partial least squares (PLS) regressions were used and predictions were validated by cross-validations. We found that the NIR spectra (best R²=0.925) were able to predict C mineralization patterns marginally better than the SCD fractions (best R²=0.911), but considerably better than the C/N ratios (best R²=0.851). In contrast, N mineralization was better predicted by SCD fractions (best R²=0.533) than the C/N ratio (best R²=0.497), which was better than NIR predictions (best R²=0.446). Although the predictions with the NIR spectra were only slightly better for C and worse for N mineralization compared to SCD fractions, NIR spectroscopy still holds advantages, as it is a much less laborious and cheaper analytical method. Furthermore, exploration of the applications of NIR spectroscopy in decomposition studies has only just begun, and offers new ways to gain insights into the decomposition process.     

Clover cover crops under-sown in winter wheat increase yield of subsequent spring barley—Effect of N dose and companion grass

Four two-year field trials, arranged in randomised split-plots, were carried out in southern Sweden with the aim of determining whether reduced N fertiliser dose in winter wheat production with spring under-sown clover cover crops, with or without perennial ryegrass in the seed mixture, would increase the clover biomass and hence the benefits of the cover crops in terms of the effect on the wheat crop, on a subsequent barley crop and on the risk of N leaching. Four doses of nitrogen (0, 60, 120 or 180 kg N ha⁻¹) constituted the main plots and six cover crop treatments the sub-plots. The cover crop treatments were red clover (Trifolium pratense L.), white clover (Trifolium repens L.) and perennial ryegrass (Lolium perenne L.) in pure stands and in mixtures. The winter wheat (Triticum aestivum L.) was harvested in August and the cover crops were ploughed under in November. The risk of N leaching was assessed in November by measuring the content of mineral N in the soil profile (0–30, 30–90 cm). In the following year, the residual effects of the cover crops were investigated in spring barley (Hordeum distichon L.) without additional N. Under-sowing of cover crops did not influence wheat yield, while reduced N fertiliser dose decreased yield and increased the clover content of the cover crops. When N was applied, the mixed cover crops were as effective in depleting soil mineral nitrogen as a pure ryegrass cover crop, while pure clover was less efficient. The clover content at wheat harvest as well as the amount of N incorporated with the cover crops had a positive correlation with barley yield. Spring barley in the unfertilised treatments yielded, on average, 1.9–2.4 Mg DM ha⁻¹ more in treatments with clover cover crops than in the treatment without cover crops. However, this positive effect decreased as the N dose to the preceding wheat crop increased, particularly when the clover was mixed with grass.     

Leaching of N,P and glyphosate from two soils after herbicide treatment and incorporation of a ryegrass catch crop

During 2005–2007, studies were carried out in two field experiments in southwest Sweden with separately tile‐drained plots on a sandy soil (three replicates) and on a clay soil (two replicates). The overall aim was to determine the effects of different cropping systems with catch crops on losses of N, P and glyphosate. Different times of glyphosate treatment of undersown ryegrass catch crops were examined in combination with soil tillage in November or spring. Drainage water was sampled continuously in proportion to water flow and analysed for N, P and glyphosate. Catch crops were sampled in late autumn and spring and soil was analysed for mineral N content. The yields of following cereal crops were determined. The importance of keeping the catch crop growing as long as possible in the autumn is demonstrated to decrease the risk of N leaching. During a year with high drainage on the sandy soil, annual N leaching was 26 kg/ha higher for plots with a catch crop killed with glyphosate in late September than for plots with a catch crop, while the difference was very small during 1 yr with less drainage. Having the catch crop in place during October was the most important factor, whereas the time of incorporation of a dead catch crop did not influence N leaching from either of the two soils. However, incorporation of a growing catch crop in spring resulted in decreased crop yields, especially on the clay soil. Soil type affected glyphosate leaching to a larger extent than the experimental treatments. Glyphosate was not leached from the sand at all, while it was found at average concentrations of 0.25 μg/L in drainage water from the clay soil on all sampling occasions. Phosphorus leaching also varied (on average 0.2 and 0.5 kg/ha/yr from the sand and clay, respectively), but was not significantly affected by the different catch crop treatments.     

A method for estimating light interception by a conifer shoot

We present an operational method for estimating the amount of PAR intercepted by a coniferous shoot. Interception of PAR by a shoot is divided into three components: the amount of radiation coming from the sky, the transmission of radiation through the surrounding vegetation, and the shoot' s silhouette area facing the direction of the incoming radiation. All three components usually vary with direction. Radiation incident from the sky consists of direct and diffuse radiation. The well-known equation of motion for the sun and Beer' s Law for atmospheric transmittance are used to simulate the directional distribution of direct sunlight for any given period of time. The diffuse component is assumed to be uniform. Meteorological field measurements are used to calibrate the absolute amounts of the direct and diffuse components. The gap fraction (proportion of visible sky) in different directions around a shoot is measured by analyzing a hemispherical fish-eye photograph, taken at the location of the shoot, with an image processing program. Similarly, the shoot silhouette area (SSA) is measured by photographing the shoot from many different directions. The measurements of SSA are interpolated by a method called trigonometric interpolation to obtain the directional distribution of SSA over the entire hemisphere. This distribution is then rotated according to the shoot' s position in the canopy. Multiplying incoming PAR, canopy gap fraction and SSA in different directions, and summing over all directions, gives an estimate of PAR intercepted by the shoot during the chosen period of time. The method is described step by step, and applied, as an example, to a shoot from a Scots pine (Pinus sylvestris L.) stand in central Finland. Differences in radiation interception properties between sun and shade shoots and their relevance to canopy-scale models are discussed.     

Near‐infrared spectroscopy for within‐field soil characterization: small local calibrations compared with national libraries spiked with local samples

The advantage of using near‐infrared spectroscopy to increase sample point density in soil mapping on farms relies on the number of conventional laboratory analyses for the calibrations being kept to a minimum. This study compared the performance of small farm‐scale calibrations (25 samples) with a larger national soil library (396 samples) and tested whether a site‐specific sample set selected from the national library, consisting of the 50 samples that were spectrally most similar to those of the local sites, could increase performance. In addition, the national library and selected subsets were augmented (‘spiked’) with up to 25 local calibration samples to test whether that had any additional effect on prediction errors and bias. Calibrations were made for predicting within‐field variation in clay, silt, sand, soil organic carbon (SOC), pH and phosphorus. Selecting a subset of samples from the national library did not improve the results compared with using the entire national library. However, spiking both libraries with local samples reduced the root mean squared error of prediction (RMSEP) considerably, mainly through a decrease in bias, and often resulted in comparable results to the local calibrations. There was a tendency for better clay and SOC predictions from spiking a reduced national library compared with spiking the entire national library, sometimes even resulting in better predictions than using the local calibrations. However, using local calibrations seems to be the best alternative for predicting soil properties at the farm or field scale, even with as few as 25 samples.     

Improving the prediction performance of a large tropical vis‐NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques

Effective agricultural planning requires basic soil information. In recent decades visible near‐infrared diffuse reflectance spectroscopy (vis‐NIR) has been shown to be a viable alternative for rapidly analysing soil properties. We studied 7172 samples of seven different soil types collected from several regions of Brazil and varying in organic matter (OM) (0.2–10.3%) and clay content (0.2–99.0%). The aim was to explore the possibility of enhancing the performance of vis‐NIR data in predicting organic matter and clay content in this library by dividing it into smaller sub‐libraries on the basis of their vis‐NIR spectra. We used partial least square regression (PLSR) models on the sub‐libraries and compared the results with PLSR and two non‐linear calibration techniques, boosted regression trees (BT) and support vector machines (SVM) applied to the whole library. The whole library calibrations for clay performed well (ME (modelling efficiency) > 0.82; RMSE (root mean squared error) < 10.9%), reflecting the influence of the direct spectral responses of this property in the vis‐NIR range. Calibrations for OM were reasonably good, especially in view of the very small variation in this property (ME > 0.60; RMSE < 0.55%). The best results were, however, found when dividing the large library into smaller subsets by using variation in the mean‐normalized or first derivative spectra. This divided the global data set into clusters that were more uniform in mineralogy, regardless of geographical origin, and improved predictive performance. The best clustering method improved the RMSE in the validation to 8.6% clay and 0.47% OM, which corresponds to a 21% and 15% reduction, respectively, as compared with whole library PLSR. For the whole library, SVM performed almost equally well, reducing RMSE to 8.9% clay and 0.48% OM.     

When is it biological control? A framework of definitions,mechanisms, and classifications

Journal of Pest Science - Biological control, or biocontrol, is the exploitation of living agents (incl. viruses) to combat pestilential organisms (incl. pathogens, pests, and weeds) for diverse...