Film mulching agriculture in arid areas is faced with pollution caused by film mulching, and currently mainly adopts the mechanized recycling of mulch film. However, residual mulch film in the soil will bind with soil under the farming environment, which affects the recycling effect. The main factors affecting the recycling of mulch film in the soil are not clear. In order to find out the specific factors, the actual dry-wet cycle water environment was simulated by using a small soil trough system based on the film lifting, separation and recycling problem of residual mulch film in the soil. The film lifting force and recycling efficiency of the residual mulch film under the action of wet-dry cycle were studied. The following results were obtained: soil compaction, film lifting angle, and the dry-wet cycle had a significant influence on the film lifting force value, indicating that the dry-wet cycle including water fertilizer had an impact on the soil structure. After mechanical loosening, the film lifting force decreased and the recycling rate of residual mulch film increased obviously. The optimal film recycling effect could be obtained under the following conditions, namely, a film lifting angle of 21.37–45.37°, the number of dry-wet cycles <3.8, a soil moisture of 22.43–23.18%, a soil compaction of 132.51–144.06 KPa, and a residual mulch film area of 45.85–64.5 cm2. The experimental results can provide technical reference for residual mulch film pollution control and mechanized recycling.

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Fast and precise estimation of the available nitrogen content in vermiculite substrates promotes prescription fertilization in desert facility agriculture. This study explored near-infrared spectroscopy for rapid detection of the available nitrogen content in vermiculite substrates in desert facility agriculture. The spectra of vermiculite matrices with different available nitrogen contents were collected through a self-assembled near-infrared spectrometer. Partial least squares expression (PLSR) established the available nitrogen spectrum prediction model optimized using different pretreatments. After pretreatment, the prediction model of the available nitrogen spectrum was simplified by adopting three feature extraction methods. A comprehensive comparison of the results of each prediction model showed that the prediction model combining the first derivative with SG smoothing pretreatment was the best. The correlation coefficients of the corresponding calibration and prediction sets were 0.9972 and 0.9968, respectively. The root mean square errors of the calibration and prediction sets were 149.98 and 159.65 mg/kg, respectively, with 12.57 RPD. These results provide a feasible method for rapidly detecting the available nitrogen content of vermiculite substrates in desert facility agriculture.

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