Drip fertigation systems are a new technology to alleviate water shortages and improve fertilizer use efficiency. Emitter clogging is the main obstacle to their application. However, few efficient, safe, and environmentally friendly methods are available to alleviate clogging. In this study, we explored the effects of magnetized water irrigation on emitter clogging at different fertilization levels. Field experiments were conducted to study the patterns and clogging characteristics of drip irrigation systems during two planting seasons. The results showed that with an increase in fertilizer application, clogging of the emitter was aggravated. Magnetization treatment effectively relieved emitter clogging, which increased the average discharge variation rate (Dra) by 4.1–29.0% and 2.6–64.4%, respectively, and decreased the dry weight (DW) of the clogging substance by 14.0–64.6% and 15.0–75%, respectively, in the two planting seasons, compared with that of the non-magnetization treatment. The composition of the main clogging substances was estimated using X-rays; the results showed that quartz, silicate, and carbonate were the dominant substances that induced emitter clogging. Magnetization treatment can reduce the content of clogging substances and is thus a possible mechanism to alleviate clogging. Our study demonstrated that water magnetization treatment is an effective, chemical-free treatment method with great potential for clogging control in drip fertigation systems.

The knowledge of crop water requirements is critical for agricultural water conservation, especially for accurate irrigation decision making in the greenhouse. Investigating the water demand pattern of the tomato in the solar greenhouse environment and constructing an appropriate irrigation decision-making model are urgently needed to improve irrigation water use efficiency. We designed four irrigation-level treatments: 100% ET₀ (T1), 85% ET₀ (T2), 70% ET₀ (T3), and 55% ET₀ (T4), and conducted a two-vegetation-season tomato planting trial under drip irrigation conditions in a solar greenhouse. The Pearson’s correlation coefficient method analyzed the intrinsic linkage and influence between soil–crop–environment and tomatoes’ water demand patterns. Indicators suitable for irrigation decision making in greenhouse tomatoes were selected, and regression functions were constructed for environmental and crop physiological parameters by combining path analysis and multiple regression methods. Finally, a fusion irrigation decision-making model was constructed by introducing a distance function in the Dempster–Shafer (D–S) theory primary probability assignment (BPA) synthesis algorithm and combining it with a triangular affiliation function. The results showed that: (1) the soil coefficient of variation was shallow > middle > deep, and tomatoes absorbed water mainly in the 0–60 cm soil layer; (2) the crop stem flow rate, net photosynthetic rate, and transpiration rate were positively correlated with irrigation water and had the highest correlation with net radiation, relative humidity, and relative humidity, with correlation coefficients of 0.9441, 0.9441, and 0.7679, respectively; (3) the constructed decision model had a significantly lower value of uncertainty than other methods, while the highest decision value could reach over 0.99, which achieved the best decision accuracy compared to other algorithms.

The production efficiency and quality of tomatoes is affected by the mode of irrigation and the nitrogen forms. This study explored the impacts of different irrigation regimes, nitrogen forms, and their coupled effects on tomato production. The various irrigation regimes were set at 50%FC~90%FC (W1), 60%FC~90%FC (W2), 70%FC~90%FC (W3), and 80%FC~90%FC (W4) Furthermore, the control (CK) group followed a conventional drip irrigation regime in the local area. Nitrogen forms in this study comprised urea-based fertilizer (urea N 32%, F1), nitrate-based fertilizer (calcium ammonium nitrate N 15%, F2), and ammonium-based fertilizer (ammonium sulfate N 21%, F3). Combining these two factors yielded 15 treatment groups. The experiment was conducted in a solar greenhouse, and the soil type was sandy loam soil. The research focused on observing the yield, quality, and water–fertilizer use efficiency of tomatoes under these 15 treatment groups. The results demonstrate that irrigation had a more significant impact on the yield and nutrient accumulation rate compared to the nitrogen forms. To comprehensively evaluate the yield, quality, and water–fertilizer use efficiency of tomatoes, a combination evaluation method was employed. W3F2 produced the highest yield, CKF2 achieved the highest comprehensive quality score, and W2F2 had the highest comprehensive water and fertilizer use efficiency score. Using the fuzzy Borda model, the evaluation information of the three dimensions was combined. W3F2 ranked first, suggesting the adoption of an irrigation control regime of 70%FC to 90%FC, along with the application of nitrate-based nitrogen fertilizer during the fruit set to the harvest stage. It presented the best performance of tomato yield, quality, and water–fertilizer use efficiency across multiple dimensions.