Modeling and optimization of laying hen manure drying process to reduce protein and ammonium-N losses by adding sodium bentonite and wheat straw.

Laying hen manure (LHM) is a major source of pollution due to its high nitrogen (N) and moisture content (MC). Therefore, reducing the MC of LHM is necessary to retain its recyclable value and reduce environmental pollution. One effective way is by incorporating sodium bentonite (SB) and wheat straw (WS) as amendments in the LHM. This work aimed to optimize the drying conditions of LHM and investigate the effect of SB and WS utilization on the dehydration rate, reduction of crude protein (CP), and reduction of ammonium-N (N [Formula: see text] -N). The response surface methodology (RSM) was used to optimize these processes. For this purpose, two sets of experiments (drying of LHM with and without SB and Ws) were designed. The independent parameters were air temperature (70, 80, and 90 °C), air velocity (1, 1.5, and 2 m s-1), layer thickness (5, 10, and 15 mm), SB (2%, 4%, and 6%), and WS (3%, 7.5%, and 12%). The results indicated that temperature and WS had the most significant influence on all responses. To maximize the dehydration rate and minimize the reduction of CP and N [Formula: see text] -N, the optimal conditions were a temperature of 78 °C, air velocity of 1 m s-1, and layer thickness of 5 mm in the first set of experiments, and a temperature of 80 °C, air velocity of 1.5 m s-1, layer thickness of 11 mm, 6% SB, and 12% WS in the second set of experiments. Under the optimum conditions, LHM treated with 6% SB and 12% WS retained 10% more CP and 58% more N [Formula: see text] -N than untreated LHM. Therefore, according to the obtained results, SB and WS are recommended as additives to reduce the CP and N [Formula: see text] -N losses of LHM during the drying process.

Prediction and variability mapping of some physicochemical characteristics of calcareous topsoil in an arid region using Vis-SWNIR and NIR spectroscopy.

Site-specific management of soils needs continuous measurements of soil physicochemical characteristics. In this study, Vis-NIR spectroscopy with two spectroscopic instruments, including charge-coupled device (CCD) and indium-gallium-arsenide (InGaAs) spectrometers, was adopted to estimate some physicochemical characteristics of a calcareous topsoil in an arid climate. Partial least squares (PLS) as linear and artificial neural networks (ANN) as nonlinear multivariate techniques were utilized to enhance the accuracy of prediction. The best predictive models were then used to extract the variability maps of physicochemical characteristics. Diffuse reflectance spectra of 151 samples, collected from the calcareous topsoil, were acquired in the visible and short-wavelength near-infrared (Vis-SWNIR) (400-1100 nm) and near-infrared (NIR) (950-1650 nm) spectral ranges using CCD and InGaAs spectrometers, respectively. The results showed that NIR spectral data of the InGaAs spectrometer was necessary to reach the best predictions for all selected soil properties. The best predictive models based on the optimum spectral range could allow us the excellent predictions of sand (RPD = 2.63) and silt (RPD = 2.52), and very good estimations of clay (RPD = 2.35) and electrical conductivity (EC) (RPD = 2.224) by ANN and very good prediction of calcium carbonate equivalent (CCE) (RPD = 2.01) by PLS. The CCD device, however, resulted in acceptable predictions of sand (RPD = 2.13, very good) and clay (RPD = 1.66, fair) by ANN, and silt (RPD = 1.78, good), EC (RPD = 1.84, good) and CCE (RPD = 1.67, fair) by PLS. Similar variability was attained between pairs of predicted maps by best models and reference-measured maps for all studied soil properties. For clay, sand, silt, and CCE, the Vis/SWNIR-predicted and equivalent reference-measured maps had acceptable similarities, indicating the potential application of low-cost CCD spectrometers for prediction and the variability mapping of these parameters.