Here we selected eight types of feedstocks to assess the effects of pyrolysis temperature (300°C, 400°C, 500°C and 600°C) and residence time (0.5, 1, 2, 4, 8 and 24 h), respectively, on the physicochemical properties. The fixed-carbon content, pH value and amount of basic functional groups in biochars increased as the pyrolysis temperature increased from 300°C to 600°C; the opposite trend was found in the biochar yield, adsorption capacity and amount of acidic functional groups. Increasing the residence time at low pyrolysis temperature (300°C) resulted in a gradual reduction in the biochar yield and progressive increase in the pH and iodine adsorption number of biochars. However, increasing the residence time at high pyrolysis temperature (600°C) had little effect on the biochar yield or pH, while it decreased the iodine adsorption number of biochars. Given the effects of pyrolysis conditions on the pH and iodine adsorption number of biochars, low-ash agricultural wastes (e.g. wheat straw) can be pyrolysed at 300°C, 2 h to produce biochar for improving alkaline soils; high-ash agricultural wastes (e.g. sweet potato vine) and forest litter (e.g. fresh leaves of apricot tree) are preferably pyrolysed at 300°C, 4 h to produce biochar for use in acidic soils.

Good water quality is important for normal production processes in industrial aquaculture. However, in situ or real-time monitoring is generally not available for many aquacultural systems due to relatively high monitoring costs. Therefore, it is necessary to predict water quality parameters in industrial aquaculture systems to obtain useful information for managing production activities. This study used back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector machine (SVM), and least squares support vector machine (LSSVM) to simulate and predict water quality parameters including dissolved oxygen (DO), pH, ammonium-nitrogen (NH₃-N), nitrate nitrogen (NO₃-N), and nitrite-nitrogen (NO₂-N). Published data were used to compare the prediction accuracy of different methods. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting DO were 0.60, 0.99, 0.99, and 0.99, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting pH were 0.56, 0.84, 0.99, and 0.57. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NH₃-N were 0.28, 0.88, 0.99, and 0.25, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM for predicting NO3-N were 0.96, 0.87, 0.99, and 0.87, respectively. The correlation coefficients of BPNN, RBFNN, SVM, and LSSVM predicted NO₂-N with correlation coefficients of 0.87, 0.08, 0.99, and 0.75, respectively. SVM obtained the most accurate and stable prediction results, and SVM was used for predicting the water quality parameters of industrial aquaculture systems with groundwater as the source water. The results showed that the SVM achieved the best prediction effect with accuracy of 99% for both published data and measured data from a typical industrial aquaculture system. The SVM model is recommended for simulating and predicting the water quality in industrial aquaculture systems.

Pot experiments were conducted to study the effects of biochar application rates (5, 10, 20 g/kg) and types of wheat straw biochar (WS), corn stalk biochar (CS) and peanut shell biochar (PS) on Suaeda salsa (S. salsa) growth and properties of saline soil in Yellow River Delta. It was found that S. salsa yield increased from 11.7 to 115 % under WS application at a range of 5–10 g/kg compared with control. As biochar rate increased to 20 g/kg, the increment decreased to 102 %. The underground biomass of S. salsa only increased at 20 g/kg rate. The S. salsa growth respond to biochar followed the order of PS > WS > CS. Biochar application generally reduced saline soil pH and the effect was more obvious for WS. The content of total organic matter increased significantly at biochar rate of 10–20 g/kg. WS at higher application rate (20 g/kg) significantly increased available phosphorus content. At the same time, CS increased more available phosphorus content while PS decreased more of exchange sodium percentage. In generally, biochar application improved saline soil quality and enhanced plant growth although these effects should be tested in long-term period.

This study assessed the effects of temperature and moisture on carbon mineralization (Cmin) in a saline soil system with biochar amendment. The dynamics of Cmin were monitored in a biochar-amended saline soil for 220days by incubation experiments under different conditions of temperature (15°C, 25°C and 35°C) and moisture (30%, 70% and 105% of the water-holding capacity). Results showed that as the incubation temperature rose, cumulative Cmin consistently increased in soil added with 0-4% biochar. The two-compartment model could well describe the dynamics of Cmin. The temperature rise increased the concentration of labile C in soil, but reduced the turnover time of labile and recalcitrant C pools and the value of temperature coefficient Q10. The response of Cmin to moisture was varying in soil amended with different levels of biochar. In the control treatment (soil alone), cumulative Cmin increased only when soil moisture was >105%. In the biochar treatments, however, 70% of water-holding capacity was optimal for Cmin, except for 2%-biochar treatment at 35°C. The findings highlight the necessity to consider the combined effects of soil moisture, temperature and the amount of biochar added for assessing Cmin in biochar-amended saline soils.

In order to investigate the effects of water-fertilizer-air-coupling drip irrigation on soil health status, including soil aeration (SA), enzyme activity (EA) and microbial biomass (MB), and its response relationship, this glasshouse experiment was conducted using tomato as the test crop, and we designed two fertilization gradients of 135 and 180 kg N·ha⁻¹, two irrigation levels of 0.6-fold and 1.0-fold of the crop-pan coefficient, and two aeration treatments of 5 and 15 mg·L⁻¹ for the three-factor and two-level completely randomized block experiment. The effects of soil dissolved-oxygen concentration, oxygen diffusion rate, soil respiration rate, soil urease, catalase, phosphatase activities and soil microbial biomass were systematically monitored and analyzed in the middle and at the end of crop growth. A structural equation model was used to comprehensively analyze the response relationship among relevant influencing factors. The results showed that coupled drip irrigation increased the soil’s dissolved oxygen, oxygen diffusion rate and soil respiration rate by 14.05%, 30.14% and 53.74%, respectively. Soil urease, catalase and phosphatase activities increased by 22.83%, 93.01% and 61.35%, respectively. The biomass of bacteria, fungi and actinomycetes increased by 49.06%, 50.18% and 20.39%, respectively. The results of a structural equation model analysis showed that water-fertilizer-air-coupling drip irrigation could effectively improve soil health status, and the descending order of influence was MB > EA > SA. This study provides scientific knowledge to reveal the improvement of soil health status by water-fertilizer-air-coupling drip irrigation.