Raphael Linker

Progressive Inhibition by Water Deficit of Cell Wall Extensibility and Growth along the Elongation Zone of Maize Roots Is Related to Increased Lignin Metabolism and Progressive Stelar Accumulation of Wall Phenolics

Water deficit caused by addition of polyethylene glycol 6000 at −0.5 MPa water potential to well-aerated nutrient solution for 48 h inhibited the elongation of maize (Zea mays) seedling primary roots. Segmental growth rates in the root elongation zone were maintained 0 to 3 mm behind the tip, but in comparison with well-watered control roots, progressive growth inhibition was initiated by water deficit as expanding cells crossed the region 3 to 9 mm behind the tip. The mechanical extensibility of the cell walls was also progressively inhibited. We investigated the possible involvement in root growth inhibition by water deficit of alterations in metabolism and accumulation of wall-linked phenolic substances. Water deficit increased expression in the root elongation zone of transcripts of two genes involved in lignin biosynthesis, cinnamoyl-CoA reductase 1 and 2, after only 1 h, i.e. before decreases in wall extensibility. Further increases in transcript expression and increased lignin staining were detected after 48 h. Progressive stress-induced increases in wall-linked phenolics at 3 to 6 and 6 to 9 mm behind the root tip were detected by comparing Fourier transform infrared spectra and UV-fluorescence images of isolated cell walls from water deficit and control roots. Increased UV fluorescence and lignin staining colocated to vascular tissues in the stele. Longitudinal bisection of the elongation zone resulted in inward curvature, suggesting that inner, stelar tissues were also rate limiting for root growth. We suggest that spatially localized changes in wall-phenolic metabolism are involved in the progressive inhibition of wall extensibility and root growth and may facilitate root acclimation to drying environments.

Optimal CO2 control in a greenhouse modeled with neural networks

Abstract CO2 enrichment in warm climates requires a delicate balance between the need to ventilate and the desire to enrich. Model-based optimization can achieve this balance, but requires reliable models of the greenhouse environment and of the crop response. This study assumes that the crop response is known, and focuses on the greenhouse model. Neural network greenhouse models were trained using data collected over two summer months in a small greenhouse. The models were reduced to minimum size, by predicting separately the temperature and CO2 concentration, and by eliminating any unessential input. The resulting models not only fit the data well, they also seem qualitatively correct, and produce reasonable optimization results. Using these models, the effect of evaporative cooling on extending the enrichment duration is demonstrated.

Fourier Transform Infrared—Attenuated Total Reflection Nitrate Determination of Soil Pastes Using Principal Component Regression, Partial Least Squares, and Cross-Correlation

This paper investigates the use of Fourier transform infrared (FT-IR) attenuated total reflectance (ATR) spectroscopy as a fast and simple way for direct determination of nitrate concentration in soil pastes, which would assist precision fertilizer placement and reduce nitrate pollution. Eight types of soils are investigated, with nitrate concentrations ranging from 0 to 1000 ppm-N. The spectral region around the nitrate band (1300–1550 cm−1) is analyzed by (1) principal component regression (PCR), (2) partial least squares (PLS), and (3) cross-correlation with reference libraries that include spectra of pure ions and/or soils. The main obstacle to accurate nitrate measurement appears to be an interfering band present in calcareous soils. This band, which may be due to carbonate, is located around 1450 cm−1 and overlaps with the nitrate band centered around 1370 cm−1. For non-calcareous soils, and in particular for light sandy agricultural soils, PLS and cross-correlation with a reference library containing only spectra of ions in water give similar results (about 8 ppm-N on dry soil basis), while PCR leads to slightly poorer results. When calcareous soils are included in the analysis, the prediction errors are about twice as large. In this case, the best results are obtained using PLS, followed by PCR, while cross-correlation with reference libraries leads to poorer results.

Characterization of Soils Using Photoacoustic Mid-Infrared Spectroscopy

This study investigates the use of photoacoustic spectroscopy (PAS) for rapid soil analysis. Photoacoustic spectroscopy requires very minimal sample preparation (air-drying), which is a major advantage compared to the more traditional transmittance technique, which requires time-consuming preparation of pellets. The amount of information contained in the PAS spectra appears to be similar to that contained in transmittance spectra, and the PAS spectra exhibit a large number of bands that can be associated with various soil constituents such as quartz, calcium carbonate, and various types of clay. Comparison with attenuated total reflection (ATR) spectra of saturated soil pastes shows that the PAS spectra provide much more information than the ATR spectra due to the strong water bands present in the latter. PAS quantitative analysis of clay, calcium carbonate, and organic matter is presented, with respective determination errors of ∼12% clay, ∼5% CaCO3, and ∼0.2% organic matter.

Robust controllers for simultaneous control of temperature and CO2 concentration in greenhouses

Abstract The present work focuses on the control of greenhouse air temperature and CO 2 concentration by means of simultaneous ventilation and enrichment. Such an operation, which may a priori seem contradictory, was shown by several authors to be required to maintain optimal temperature and CO 2 setpoints. The control process is divided into two distinct control loops, the first maintaining the temperature by adjusting the ventilation, and the second maintaining the CO 2 concentration by adjusting the enrichment. The CO 2 concentration controller assumes the ventilation rate to be constant and approximately known over 2-min intervals. Implementation in an experimental greenhouse shows the ability of the controllers to meet the requirements.

Brief paper: Hamilton-Jacobi-Bellman formalism for optimal climate control of greenhouse crop

The paper describes a simplified dynamic model of a greenhouse tomato crop, and the optimal control problem related to the seasonal benefit of the grower. A HJB formalism is used and the explicit form of the Krotov-Bellman function is obtained for different growth stages. Simulation results are shown.

Discrimination of Soil-Borne Fungi Using Fourier Transform Infrared Attenuated Total Reflection Spectroscopy

Fourier transform infrared (FT-IR) attenuated total reflection (ATR) spectroscopy was used to discriminate five commonly encountered soil-borne fungi that cause severe economic damage to agriculture: Colletotrichum, Fusarium, Pythium, Rhizoctonia, and Verticillium. Contrary to previous studies related to microorganism discrimination using FT-IR-ATR spectroscopy, the pathogen samples were not dried on the ATR crystal, which is a time-consuming operation. Rather, after removing some pathogen filaments from the solution using tweezers, these were placed directly on a flat ATR crystal and pressure was applied using a pressure clamp. Following water subtraction, baseline correction, and normalization of the spectra, principal component analysis was used as a data-reduction step and canonical variate analysis was used for discrimination. Discrimination was performed at the genus level and at the strain level for Colletotrichum. For discrimination between the five fungi at the genus level, the success rate for the validation samples ranged from 75% to 89%. For discrimination between the two Colletotrichum strains, the success rate was 78%. Comparison with spectra of similar fungi dried on the ATR crystal showed that both types of spectra were very similar, indicating that drying the samples on the ATR crystal is not required and can be replaced by mathematical post-processing of the spectra. For routine analyses that involve rapid screening of very large amounts of samples, this approach allows for increasing significantly the number of samples that can be analyzed daily.

Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands

Abstract Leaf pigment content provides valuable insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast, nondestructive method for pigment estimation. A number of methods were used previously for estimation of leaf pigment content, however, spectral bands employed varied widely among the models and data used. Our objective was to find informative spectral bands in three types of models, vegetation indices (VI), neural network (NN) and partial least squares (PLS) regression, for estimating leaf chlorophyll (Chl) and carotenoids (Car) contents of three unrelated tree species and to assess the accuracy of the models using a minimal number of bands. The bands selected by PLS, NN and VIs were in close agreement and did not depend on the data used. The results of the uninformative variable elimination PLS approach, where the reliability parameter was used as an indicator of the information contained in the spectral bands, confirmed the bands selected by the VIs, NN, and PLS models. All three types of models were able to accurately estimate Chl content with coefficient of variation below 12% for all three species with VI showing the best performance. NN and PLS using reflectance in four spectral bands were able to estimate accurately Car content with coefficient of variation below 14%. The quantitative framework presented here offers a new way of estimating foliar pigment content not requiring model re-parameterization for different species. The approach was tested using the spectral bands of the future Sentinel-2 satellite and the results of these simulations showed that accurate pigment estimation from satellite would be possible.

Description and calibration of a dynamic model for lettuce grown in a nitrate-limiting environment

A three-compartment lettuce model designed to predict growth and nitrate concentration under severe nitrogen stress conditions is described and calibrated using data of N-limitation experiments. Since the limited amount of data does not allow calibration of all the model parameters, sensitivity analysis was used to determine which parameters should be estimated using this data. The calibrated model predicts quite well the main observations associated with severe nitrogen stress: reduced growth, fast depletion of nitrate, gradual reduction of reduced-N content, and considerable increase of dry matter content. After being deprived of nitrate for 21 days, stressed plants are five to ten times smaller than unstressed plants, and their dry matter content is two to three times higher than normal. Nitrate concentration of N-stressed plants drops to an insignificant level within three days, while reduced-N content decreases steadily throughout the experiment. All these observations are correctly predicted by the model.

Determination of Soil Nitrate and Water Content Using Attenuated Total Reflectance Spectroscopy

Direct determination of nitrate and soil moisture can significantly improve N-application management and thus reduce N-derived environmental pollution related to agriculture. Several studies have shown that Fourier transform infrared attenuated total reflectance (FT-IR/ATR) spectroscopy could be used to estimate the nitrate content of standardized soil pastes. Paste standardization appeared to be the main obstacle to in situ application of this approach, and the present study shows how FT-IR/ATR can be used to estimate both water content and nitrate concentration of field soil samples. Water content and nitrate concentration are determined sequentially using two subsamples of the initial soil sample. An a priori determined amount of highly concentrated nitrate solution is added to the first subsample and the ATR spectrum of this paste is used to estimate the sample water content. It is then possible to calculate the amount of water that should be added to the second subsample so that the resulting paste is very close to the ideal standard paste. Nitrate concentration, mg [N]/kg [dry soil], is estimated using the FT-IR/ATR spectrum of this second paste. Results are presented for a laboratory experiment with four agricultural soils, as well as for a field trial with a calcareous soil. For water content, the determination errors range from 0.01 to 0.02 g [water]/g [dry soil]. For nitrate concentration, the errors for three of the soils range from 5.9 to 8.4 mg [N]/kg [dry soil], while for the fourth, calcareous clay soil, the determination error is 13.6 mg [N]/kg [dry soil]. The determination errors obtained for the field trial are similar to the ones obtained for a similar soil under laboratory conditions, which shows the potential usefulness of the approach for improving N-application management and reducing environmental pollution.