Irrigation and fertilization management are important for controlling agricultural soil salinity and increasing productivity in extremely arid regions. The objective of this research was to evaluate the effects of long‐term drip irrigation and organic fertilizer application on soil salinity, fruit quality and yield of jujube [Ziziphus ziziphus (L.) Karst]. We established a 7‐yr field experiment in a desert‐oasis area in Northwest China. Six treatments were performed: CK (conventional irrigation, no fertilizer), CIMF (conventional irrigation, mineral fertilizer), CIOF (conventional irrigation, organic fertilizer), DI (drip irrigation, no fertilizer), DIMF (drip irrigation, mineral fertilizer), and DIOF (drip irrigation, organic fertilizer). The results showed that soil organic carbon (SOC) concentration in soil treated with organic fertilizer increased gradually over years. Treatment DIOF favored the accumulation of SOC with the highest content and stock after 7 yr. Lower salt content in root zone was found in DIOF after 7 yr. The yield of DIOF increased annually and reached an equilibrium level after the fourth year (14.88 to 16.69 Mg ha−1). Total carbohydrate and vitamin C (Vc) of DIOF of fruit were all significantly higher than CIOF and DIMF. The boosted regression tree showed that SOC content had the highest relative contribution rate to jujube yield. Path analysis identified the most important factor affecting SOC in the drip‐fertigation system was the input of organic fertilizer, with path coefficient 0.65. Long‐term combination of drip irrigation and organic fertilizer application would be an effective strategy maintaining productivity and improve fruit quality in extremely arid areas.

This study aimed to develop a robust predictive model for tetracycline (TC) adsorption onto biochar (BC) by employing machine learning techniques to investigate the underlying driving factors. Four machine learning algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN), were used to model the adsorption of TC on BC using the data from 295 adsorption experiments. The analysis revealed that the RF model had the highest predictive accuracy (R2 = 0.9625) compared to ANN (R2 = 0.9410), GBDT (R2 = 0.9152), and XGBoost (R2 = 0.9592) models. This study revealed that BC with a specific surface area (S (BET)) exceeding 380 cm3·g−1 and particle sizes ranging between 2.5 and 14.0 nm displayed the greatest efficiency in TC adsorption. The TC-to-BC ratio was identified as the most influential factor affecting adsorption efficiency, with a weight of 0.595. The concentration gradient between the adsorbate and adsorbent was demonstrated to be the principal driving force behind TC adsorption by BC. A predictive model was successfully developed to estimate the sorption performance of various types of BC for TC based on their properties, thereby facilitating the selection of appropriate BC for TC wastewater treatment.

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